Dear {{first_name|Causal Inference in Statistics Enthusiast}},

Short and sweet today.

[CODE + VIDEO] Your Model is Accurate (and Wrong)—Causal Inference for Data Scientists

A few weeks ago, I held a free one-hour interactive webinar titled Your Model is Accurate (and Wrong)—Causal Inference for Data Scientists.

We covered simple examples where highly accurate well-fitting models (R-square = 0.99) hide huge bias.

You don’t need a causal inference Ph.D to understand how confounder bias, collider bias, and mediation can be understood using simple data structures called Directed Acyclic Graphs.

Get exclusive access to the code notebook with the simulations here

Enjoy!

If you want to go deeper, Chapter 3 of my book Causal Inference in Statistics with Exercises, Practice Projects, and R Code Notebooks covers these ideas in much more depth (50 pages and a code notebook simulating many more structures). The 1st chapter is free.

What's The Plan?

• [CODE + VIDEO] Your Model is Accurate (and Wrong)— …

• [BOOK] Writing Updates

• [COURSE] New Features

• Join Our Causal Inference Communities

  • Heading 3

[BOOK] Writing Updates

What I’ve done in the past few months towards my book.

• Created an online version of the book accessible to those who pre-ordered. Check out a video preview here.

• Developed new material for Chapter 4: Adjusting for Measured Confounding and Propensity Score Methods.

• Worked on the R code notebook for Case-study: The Right Heart Catheterization Dataset, which will serve to illustrate implementations of adjustment methods such as regression, stratification, matching, and different propensity score techniques.

• Started planning for converting the case-studies in Python.

Check out the first chapter for free with the button below.

[COURSE] New Features

A few new things regarding the course Introduction to Biostatistics: Unlocking the Secrets of Biostatistics with R, Python, and Real-World Data:

• Free lessons

• A completion certificate

• New 5-star reviews

Thanks, Henry! So glad that this course went above your expectations 🙂 

Join Our Causal Inference Communities

Causal Inference Linkedin Group

Join 3.3k+ Causal Inference Enthusiasts

https://www.linkedin.com/groups/13065272/

Causal Inference Nerds 🤓 Discord Community

Q&A Forum, Meme channel, Office hours voice channel, Journal Club, and more!

https://discord.gg/2xKvHMhYDg

Dear {{first_name|Causal Inference in Statistics Enthusiast}},

I have three (3) quick things for you this month.

[WEBINAR] Causal Inference Workshop Upcoming

If you've ever wondered why a perfectly good model can give you the wrong causal estimate, this one's for you.

I'm hosting a free live webinar with Packt Publishing where we'll simulate three causal structures (a confounder, a mediator, and a collider) and watch how the estimates can break in real time.

No heavy theory. Just live code, clear intuition, and a framework you can use immediately. Stick around to the end for a chance to win a free copy of my book Causal Inference in Statistics with Exercises, Practice Projects, and R Code Notebooks.

• April 20th, 2026

• 12:00 PM EDT

• Interactive + Q&A

What's The Plan?

• [WEBINAR] Causal Inference Workshop Upcoming

• Recap: What We Covered at The Causal Mindset Linke …

  • Prediction ≠ causation…

  • …and making model explainable doesn't make it caus …

  • The cold shower problem

  • LLMs and causality

• My Monthly Causal Inference Book Review

  • A Thoughtful Quote

Recap: What We Covered at The Causal Mindset LinkedIn Live + Youtube Link Available

Having Fun Chatting About Causal Inference With Quentin!

I had a great time speaking with Quentin Gallea, PhD about the Causal Mindset! Here is a quick recap.

Prediction ≠ causation…

Quentin opened with something I think about constantly: predictive ML is incredibly powerful, but it exploits correlations. It doesn't tell you what will happen if you intervene. His example was visceral—using a fever as a lever to treat sepsis could kill the patient. The same logic applies to business decisions every day, just with less obvious consequences.

…and making model explainable doesn't make it causal

A model can be fully interpretable and still be completely wrong from a causal standpoint. Quentin showed how including a collider in a predictive model can make it more accurate while making the causal interpretation more misleading. A strong predictor is not a cause.

The cold shower problem

We spent time on a fascinating real-world example from Quentin's book: a well-known Dutch RCT showing cold showers reduced work-related sick leave by ~33%. Sounds convincing. But dig into the mechanism—placebo effects, observer effects, self-reporting bias, no blinded control—and the evidence looks a lot shakier. The takeaway: you can challenge peer-reviewed research without writing a single equation. Domain knowledge and scientific common sense go a long way.

LLMs and causality

We also touched on where LLMs fit into causal workflows. They can help structure and challenge causal graphs, and there's promising research on LLM-augmented causal discovery. But they shouldn't be the ones making causal calls. That still requires a human with domain knowledge and a rigorous framework.

A big thank you to Quentin for the generous conversation—and to everyone who joined live, asked questions, and engaged in the comments. Two lucky attendees also walked away with copies of Quentin's book, The Causal Mindset.

If you missed it, the resources Quentin shared during the talk are worth bookmarking:

• Causal Inference for the Brave and True — Matheus Facure (free)

• Causal Mixtape — Scott Cunningham (free)

• Causal Machine Learning for Predicting Treatment Outcomes — Nature Medicine

• Making Sense of Sensitivity — Cinelli & Hazlett

My Monthly Causal Inference Book Review

I love this introduction to causal inference.

Rosenbaum is an important contributor to the potential outcomes framework. He worked alongside Rubin to develop, for example, the propensity score approach.

He had previously written a short book called Causal Inference, which I also highly recommend.

This book, Observation and Experiment, is longer and goes deeper, but doesn’t sacrifice ease-of-understanding, storytelling, and real-world examples. Together, these features make Rosenbaum’s approach highly suitable to beginners and experienced practitioners alike.

The highlights, imho, are found in his case-studies, for example, a study done after an important Chilean earthquake that showcases how observational studies can meet limits that simply cannot be perfectly overcome, but must be acknowledged.

A Thoughtful Quote

“... even a reasonably compelling observational study may turn out, in light of subsequent research, to have reached an erroneous conclusion.

Sometimes a reasonably compelling observational study prompts investigators to perform a randomized trial, and sometimes the trial does not support the conclusions of the observational study. At other times, several reasonably compelling observational studies point in incompatible directions.

When ethical or practical constraints force scientists to rely on observational studies, it is not uncommon to see a decade or more of thrashing about, a decade or more of controversy, conflicting conclusions, and uncertainty. This can be true even when the studies themselves are well designed and executed.

Can an observational study be more than reasonably compelling? Arguably, it has happened once or twice, but reasonably compelling studies are rare to begin with.”

Paul Rosenbaum, Observation and Experiment (emphasis mine)

[COURSE] Introduction to Biostatistics—Unlocking the Secrets of Biostatistics with R, Python, and Real-World Datasets

My online course—built over more than a year with Alex Molak—is finally available!

We truly believe this course is unique, and let me tell you why.

Forget decision trees, flowcharts, and test-picking rules. This course will make you think like a statistician, a causally-aware one. Featuring:

• Over 10h of video lectures

• Podcast-style discussions between Alex and I about philosophical topics, our thoughts on non-parametric testing and log-transformations, and more

• Exercises and projects with real narratives and real datasets

• Coding throughout, with both R and Python

• Unique perspective combining traditional statistics material with discussions about experimentation vs. observations, causal inference, thought-experiments

• Certification path available for those that meet the requirements

Lesson 1—Introduction and Variable Types

• A Salmon, a Neuroscientists, and the “Best Practice” That Went Wrong

• Causality, Causal Inference, and Statistics

• AI, Machine Learning, and Data Science

• Biostatistics Today

Lesson 2—Descriptive Statistics

• Distributions: From Water Molecules to Supercomputers

• Mean, Medians, Percentiles, Robust statistics, and more

Lesson 3-6 + Capstone Project

• Visualizing Data

• Probability Distributions

• Hypothesis Testing

• Modelling

• Capstone Project

The live cohort we ran over last summer included: medical writers, engineers in big tech, biologists in biotech, and more.

This course is perfect if you’re tired of oversimplified heuristics and want to learn statistics properly, once and for all.

As a newsletter subscriber, you receive an exclusive 15% Discount, valid for a limited time.

Ready to think like a statistician?

What's The Plan?

• [COURSE] Introduction to Biostatistics—Unlocking t …

• [LINKEDIN LIVE WEBINAR] The Causal Mindset: Causal …

• My Monthly Book Recommendation

• Mann-Whitney Is Not About Medians

[LINKEDIN LIVE WEBINAR] The Causal Mindset: Causal AI For Decision-Making

Excited to announce the upcoming Linkedin Live Event with Quentin Gallea, PhD!

We will discuss:

• Measuring the Impact of Causal AI (prediction + causaity)

• Discovering Levers to Drive Business Outcomes

• Audience Q&A

• And more…

Broadcasting live on Linkedin and Youtube March 18, 12PM EDT/ 5PM CET/ 9AM PDT—excited to see you all!

Use the button below to go to Linkedin and click Attend to get a reminder

My Monthly Book Recommendation

A Deep-Dive Into Clinical Trials For Drug Development

Stephen Senn is an authority when it comes to pharmaceutical clinical trials.

As you know, statistics is not without controversy.

One-by-one, Stephen tackles methodological controversies in one of the most statistically sophisticated industries—drug development—and shares his perspectives with wit, incisive criticism, appropriate context, and more.

Of course, he wouldn’t be a true statistician if he didn’t include impactful visualizations, simulated data, and mathematical derivations to defend his points.

This book is advanced, and might not be easy to grasp in a first pass. But it will make you see and feel how deep and nuanced statistics can be.

One of my favorite books of all-time, I find myself going back to it early and often, when I need to think deeply about the challenges of my profession.

Mann-Whitney Is Not About Medians

This paper takes two angles to discuss what the (Wilcoxon)-Mann-Whitney U Test (WMW) is really about:

1. Mathematical manipulation of the test statistics

2. Simulated data showing extremely counterintuitive results

Learn why WMW is not about medians. You might just reach for the good old Welch’s T-test more often and save your data analyses—an approach I extensively discuss in my Intro to Biostatistics Online Course.

[3-Day Stats Challenge] Why Most Test-Picking Rules Are Wrong—and What to Do About It

Statistics is often taught using decision trees, flowcharts, and test-picking rules. Unfortunately, professional statisticians in industry and academia do not work like this—statistical reasoning is simply not based on linear decision-making.

Want to learn how to develop a professional statistician’s frame of mind?

Alex Molak and I are holding a free 3-day biostatistics challenge to do just that—and you’re invited!

• When: Feb 25-26-27, from 12-1:30PM, Eastern Time (6-7:30PM CET and 9-10:30AM Pacific)

• Who: Anybody interested in statistics, from beginners to the more experienced

• What: Lectures, Q&A, and a statistics challenge with a walkthrough to go over important aspects

• Where: Join the mailing list to receive the exclusive Zoom code

[Day 1] Rethinking the Problem

• All Models Are Wrong—But Some Are Useful

• Modeling Starts Earlier Than Most People Think

• Before We Test: Are We Answering the Right Question?

[Day 2] Design Beats Analysis

• Is Design More Important Than Analysis?

• Case Study

• Student Task

[Day 3] From Struggle to Structure

• Common approaches and pitfalls in accomplishing the task

• Solution Walkthrough

• Generalizing the Approach

• Applying the framework to other problems

• Special Offer

• Q&A

What's The Plan?

• [3-Day Stats Challenge] Why Most Test-Picking Rule …

• [NEW OFFER] Training and Workshops

• [BOOK WRITING UPDATES]

• A Book I’ve Read Recently

• [PODCAST] Live Events For 2026

• [MEMES] Felt Frisky For February

[NEW OFFER] Training and Workshops

In 2025, my discussions with dozens of practitioners from industry—medical devices, biotech, and pharma—taught me two things.

1. There is a shortage of skill in industry, specifically for causal inference for real-world evidence.

2. My consulting experience in a diverse set of applications together with my university teacher experience make me a unique trainer.

I began offering training and workshops in causal inference, real-world evidence, and biostatistics.

My approach is engaging, warm, and thorough. I can even leave your company with a rich set of educational material (videos, notes, Q&As, etc.) for continuous improvement of new cohorts.

The response has been overwhelming and I’ve since trained

• Pharma RWE teams

• Industry scientists in biotech

• Research Labs

• And more!

Ready to discuss how I can help?

I’m almost fully-booked, but could take on an additional workshop or training in 2026.

[BOOK WRITING UPDATES]

Chapter 5 was released in 2025.

Chapters 4 and 7 are both about halfway done. Together, Chapters 1-5 and 7 represent over 65% of the book—the finish is close!

If you pre-order my Causal Inference in Statistics, with Exercises, Practice Projects, and R/Python Code Notebooks, you now receive:

• A 230 page PDF with the 4 chapters

• An online version with the 4 chapters (see video below)

• 6 R code notebooks

• Receive the new parts of the book upon release (2 new chapters + 5 additional notebooks, coming soon) and any updates to the material via Github

• A pre-order for the physical book once it is complete

Writing this book has been a tremendous challenge: balancing client needs in consulting work, my personal obstacles (2025 was not only hectic for geopolitics, my personal life was also), and actually writing a quality book, have been at the core of my professional activities.

I say this to say how thankful I am for your support 💜

You can order now—you will be the first to receive the new material when it launches 😋 

A Book I’ve Read Recently 

A Technical Textbook

Assumptions are tools to help us deduce A from B.

In causal inference, we use causal assumptions (e.g. no confounding) to link the observed reality of the data (i.e. correlation) with the unobservable counterfactual reality we care about (i.e. causality).

Often, assumptions are taught as if they have to be true. Of course, in a strict sense, this is right: the theory falls apart when the assumptions aren't true, since it relies on mathematical theorems.

In short, with the right assumptions, we can say that correlation IS causation, thanks to the power of mathematics.

Yet in reality, it is impossible for these assumptions to be true. For example, the complexity of the world forbids us from measuring every single confounder accurately (let alone model them correctly).

Any experienced practitioner will know that they can get away with this state affairs: they can judge with some confidence when and how their methods hold up even when their assumptions are not met.

And the best of the best will not only try to convince you that their judgment is sound: they will show you!

Methods from the family of sensitivity analysis, and what epidemiologists call Quantitative Bias Analysis (QBA), are ways to quantify the potential bias in your results due to unmet causal assumptions.

In this day and age, any serious causal study SHOULD be using such methods—it is no longer sufficient to estimate a quantity and hope for the best.

Books like this one formalize these approaches.

Chapter 5 of my Causal Inference in Statistics book covers QBA—it’s available right now.

[PODCAST] Live Events For 2026

I’ve done many live events over the year, all available on Youtube. I’m kicking off 2026 with an exciting guest to be announced soon.

Any guests you would love to see? Let your imagination run wild, and let me know!

[MEMES] Felt Frisky For February

I saved the best for last.

I Must Admit I Use Excel, Lowkey Microsoft’s Best Product (And I Hate It)

I Really Don’t Like Excel For Statistics—And You Shouldn’t Either

Don’t Be Like Mr. Incredible

Dear {{first_name|Causal Inference in Statistics Enthusiast}},

[COURSE] Introduction to Biostatistics — Exclusive Coupon Code

We finally did it: all the material to the online course is recorded. We are editing the content and preparing the user experience on the platform.

Expect a release in February 2026 🎉

To celebrate this important milestone, Aleksander Molak and I sat down for a discussion. He asked me the one thing I want students to take away from the Intro to Biostats online course. My answer:

We decided to take the celebration even further by offering a 36$ discount until Monday, Dec 1, 23:59 UTC. The price will never be lower.

What's The Plan?

• [BOOK] Chapters 1,2, 3, and 5 Now Available

• [PODCAST] Live Events For 2026

• [PODCAST] Sneak-Peak On My Book Writing Process

• [CODE NOTEBOOK] Use Robust Estimation Procedures

• [BOOK] An Overlooked Causal Inference Masterpiece …

• [COMMUNITY] Collaborators

• [CONSULTING] Training and Workshops

• [FREE] Resources You Might Like

[BOOK] Chapters 1,2, 3, and 5 Now Available

To those who preordered: Chapters 1, 2, 3 are now supplemented with Chapter 5 —The Art of Observational Studies: Pragmatically Bridging Causal Frameworks!

• This 55-page monster chapter includes a case-study code notebook, dozens of thoughtful exercises, guidelines and frameworks for conducting observational studies, and discussions about target-trial emulation, negative controls, well-defined interventions, and more.

If you pre-order my Causal Inference in Statistics, with Exercises, Practice Projects, and R/Python Code Notebooks, you now receive:

• A 230 page PDF with the 4 chapters

• An online version with the 4 chapters (see video below)

• 6 R code notebooks

• Receive the new parts of the book upon release (2 new chapters + 5 additional notebooks, coming soon) and any updates to the material via Github

• A pre-order for the physical book once it is complete

In case you missed it, a preview of the online version:

You can order now—you will be the first to receive the new material when it launches 😋 

[PODCAST] Live Events For 2026

I’ve done many live events over the year, all available on Youtube. I’m kicking off 2026 with an exciting guest to be announced soon.

Any guests you would love to see? Let your imagination run wild, and let me know!

[PODCAST] Sneak-Peak On My Book Writing Process

Ever wonder how writing a technical book feels like? I lift the curtain on this life-changing experience in Dr. Alexander Schacht’s podcast The Effective Statistician

Behind the Scenes of Writing a Book About Casual Inference

The Effective Statistician - in association with PSI · Episode

open.spotify.com/episode/0oBvz1iLGDxsQuY6imZj9r?si=3weVL8ZQSQSSd65ZTaiGfg

[CODE NOTEBOOK] Use Robust Estimation Procedures

Dont’t test your assumptions: use robust procedures, clever study design, and sensitivity analysis to make your assumptions credible.

Thanks to Adrian Olszewski for the inspiration

I wrote a blog post about Welch’s t-test, including a tutorial, a simulation study, and a free code notebook.

[BOOK] An Overlooked Causal Inference Masterpiece 

Shadish, Cook, and Campbell - the latter having pioneered the Regression Discontinuity Design (RDD) in the 1960s, a quasi-experimental approach that revolutionized field research in social science - present their conceptual theory of generalized causal inference.

Me holding my edition of Shadish, Cook, and Campbell (it’s had a hard life)

This book has close to 0 math and barely any formal statistics, being instead replete with real-world examples drawn from social sciences and policy research literatures, and deep, incisive insights into scientific research methodology.

Amongst other groundbreaking concepts, I particularly appreciated their typology of validity, which goes beyond the internal validity-external validity dichotomy for inference, but instead extends validity to also encompass statistical conclusion validity and construct validity, which are particularly important in biomedical and social sciences.

My favorite passage from the book is: "Validity is a property of inferences. It is not a property of designs or methods, for the same design may contribute to more of less valid inferences under different circumstances."

I incorporate insights from this masterpiece in Chapter 7 of my own Causal Inference in Statistics, which covers quasi-experimental designs.

I’m releasing Chapter 7 next.

[COMMUNITY] Collaborators

The Biostatistics Group is looking for new collaborators for 2026.

What’s in it for you? 

• You showcase your expertise, including a short author profile and a link of your choice.

• Our website gets 1k+ clicks a month—this is a great way to drive traffic to a link of your choice

[CONSULTING] Training and Workshops

You may already know that I’m a University Lecturer and that my passion is teaching 💜 

I offer training and workshops in causal inference, real-world evidence, and biostatistics.

I’ve trained

• Pharma RWE teams

• Industry scientists in biotech

• Research Labs

• And more!

My approach is engaging, warm, and thorough.

Ready to discuss how I can help?

I’m almost fully-booked, but could take on an additional workshop or training.

[FREE] Resources You Might Like

Causal Inference Nerds 🤓 Discord Community

Q&A Forum, Meme channel, Office hours voice channel, Journal Club, and more!

https://discord.gg/2xKvHMhYDg

biostatistics.ca Newsletter!

Stay at the forefront of biostatistics and industry with our brand new newsletter

https://www.biostatistics.ca/newsletter/

FREE Github Library of Code Notebooks

We created a free Github repo with all our code notebooks, check it out!

https://www.biostatistics.ca/github-library/

Causal Inference Linkedin Group

Join 3.5k+ Causal Inference Enthusiasts

www.biostatistics.ca/the-biostatistics-roadmap-or-how-to-become-a-great-biostatistician

Dear {{first_name|Causal Inference in Statistics Enthusiast}},

For the past year, Alex Molak and I have been quietly building something I’m really proud of: an Introduction to Biostatistics online course designed to change how statistics is taught.

This course was born from a frustration I’ve had for more than a decade: the ideas that truly spark curiosity in statistics—causality, modern inference, deeper statistical thinking—rarely show up in Stats 101.

Alex and I wanted to fix that.

Our goal was simple: teach statistics in a way that opens doors, not closes them. A way that makes students feel excited about what’s possible, not intimidated.

The course includes 6 core lectures broken into short, engaging modules, packed with R and Python code, case studies, thought experiments, visualizations, quizzes, and real-world data:

1. Course Presentation - Biostatistics today, AI, Machine Learning, Causality

2. Descriptive Statistics

3. Visualizing Data

4. Probability Distributions

5. Hypothesis Testing

6. Modelling Relationships Between Variables

Alex asked me, “What’s the one thing you want students to walk away with?” My answer below:

Dear {{first_name|Causal Inference in Statistics Enthusiast}},

[NEW] Chapter 5 on Observational Studies Now Available

To those who preordered Chapter 5 —The Art of Observational Studies: Pragmatically Bridging Causal Frameworks is now available!

• This 55-page monster chapter includes a case-study code notebook, dozens of thoughtful exercises, guidelines and frameworks for conducting observational studies, and discussions about target-trial emulation, negative controls, well-defined interventions, and more.

If you pre-order my Causal Inference in Statistics, with Exercises, Practice Projects, and R/Python Code Notebooks, you now receive:

• A 230 page PDF with the 4 chapters

• An online version with the 4 chapters (see video below)

• 6 R code notebooks

• Receive the new parts of the book upon release (2 new chapters + 5 additional notebooks, coming soon) and any updates to the material via Github

• A pre-order for the physical book once it is complete

In case you missed it, a preview of the online version:

You can order now—you will be the first to receive the new material when it launches 😋 

What's The Plan?

• [NEW] Chapter 5 on Observational Studies Now Avail …

• The Causal Inference Roadmap

• [DISCORD] Causal Inference🤓 Journal Club #5 Recap

  • Evaluating Causal Claims in News

• Statistics Book Reviews

• [COURSE] Introduction to Biostatistics — Exclusive …

• Consulting—Training and Workshops

• Other (FREE) Resources You Might Like

The Causal Inference Roadmap

The Biostatistics Group quietly released The Causal Inference Roadmap, a detailed guide with books, articles, and online courses to learn causal inference skills.

Have any feedback? Reply to this email or write to admin@biostatistics.ca.

[DISCORD] Causal Inference🤓 Journal Club #5 Recap

Evaluating Causal Claims in News

Katie Davis presented on using Bradford Hill's viewpoints and modern causal frameworks to critically assess health research reported in the media.

Key Framework: Bradford Hill's 9 viewpoints (1965) including temporality, consistency, dose-response, and plausibility - plus modern tools like DAGs and Causal Pies.

Case Study: Katie dissected a viral intermittent fasting study claiming 8-hour eating windows increase cardiovascular death risk. Major red flags identified:

• Exposure based on only 2 days of dietary recalls, assumed to represent years of habits

• Temporal ambiguity: did poor health cause eating patterns?

• 70% of short-window group skipped breakfast vs 12% of controls - then adjusted for breakfast skipping

• Multiple testing issues

Key Takeaway: Always ask "In whom, by how much, and under what conditions?" and critically evaluate exposure measurement before accepting causal claims.

Next Session: details coming soon! Join us 🤓

Once in the Discord ➡️ voice channels ➡️ journal-club or book-club, the events will be held there!

The community hosts causal AI founders looking for co-founders, job postings, clubs, memes, and other exclusive community features—all free!

Statistics Book Reviews

Over time, The Biostatistics Group has developed many comprehensive lists of book reviews. Check them out.

• Best Survival Analysis Books

• Best Nonparametric Statistics Books

• Best Regression Modelling Books

• Best Statistical Sampling Theory Books

• Best Introduction to Statistics and Data Analysis Books

• Best Advanced Statistics Books

• Best Causal Inference Books

• Best Design of Experiments Books

• Best Epidemiology Books

[COURSE] Introduction to Biostatistics — Exclusive Discount

Aleksander Molak and I have finished the Founding Cohort.

We’re currently working hard on creating the material for the self-paced version that will be available online. What do you think about my setup?

Recording the Intro to Biostats course!

We will offer an exclusive discount to those who join the waiting list, as a token of appreciation for their patience.

Consulting—Training and Workshops

You may already know that I’m a University Lecturer and that my passion is teaching 💜 

I offer training and workshops in causal inference, real-world evidence, and biostatistics.

I’ve trained

• Pharma RWE teams

• Industry scientists in biotech

• Research Labs

• And more!

My approach is engaging, warm, and thorough.

Ready to discuss how I can help?

I’m almost fully-booked, but could take on an additional workshop or training.

Other (FREE) Resources You Might Like

Causal Inference Nerds 🤓 Discord Community

Q&A Forum, Meme channel, Office hours voice channel, Journal Club, and more!

https://discord.gg/2xKvHMhYDg

biostatistics.ca Newsletter!

Stay at the forefront of biostatistics and industry with our brand new newsletter

https://www.biostatistics.ca/newsletter/

FREE Github Library of Code Notebooks

We created a free Github repo with all our code notebooks, check it out!

https://www.biostatistics.ca/github-library/

Causal Inference Linkedin Group

Join 3.3k+ Causal Inference Enthusiasts

www.biostatistics.ca/the-biostatistics-roadmap-or-how-to-become-a-great-biostatistician

Dear {{first_name|Causal Inference in Statistics Enthusiast}},

[NEW] Online Version Available to Book Readers

My book now has an exclusive online version for those who pre-ordered, thanks to the dedicated work of my co-author, Bastien Chassagnol.

He worked hard behind the scenes to make the computational aspects of publishing a book both in PDF and online feasible. Thank you, Bastien!

You should have received your online access codes by email—reach out if you need anything. Here’s a sneak peak:

My roadmap for the book:

1. Next month: Release Chapter 5 —The Art of Observational Studies: Pragmatically Bridging Causal Frameworks

   1. This 55-page monster chapter includes a case-study code notebook, dozens of thoughtful exercises, guidelines and frameworks for conducting observational studies, and discussions about target-trial emulation, negative controls, well-defined interventions, and more.

2. By the end of the year: Release Chapter 4 — Adjusting for Measured Confounding and Propensity Score Methods—, and Chapter 7—Quasi-Experimental Designs: Simulated Randomness.

   1. Chapter 7 is beautiful and covers a wide-array of quasi-experimental designs traditionally developed in social sciences.

   2. Chapter 4 contains an informative case-study where we reproduce a famous observational study and apply modern adjustment methods to it.

   3. Chapter 7 will likely come before Chapter 4, as I’ve been more inspired about this material lately.

3. Early 2026: Chapter 6—Causality by Proxy: The Logic of Instrumental Variables

   1. Randomization as an instrument, non-compliance in clinical trials, Mendenlian randomization, and a fascinating case-study. Stay Tuned.

4. Polishing the already-published material, ironing out inconsistencies between the web/PDF versions, and building out the outlines for the rest of the book.

5. Chapter 8—Structural Equation Models and Path Analysis, Chapter 9—Mediation and Interaction Analysis, Chapter 10—Time-Varying Confounding, and Chapter 11—Causal Artificial Intelligence and Machine Learning, will be the focus of my attention for early-mid 2026!

If you pre-order my Causal Inference in Statistics, with Exercises, Practice Projects, and R/Python Code Notebooks, you now receive

• A 158 page PDF with the first 3 chapters

• An online version with the first 3 chapters

• 5 R code notebooks

• Receive the new parts of the book upon release (3 new chapters + 6 additional notebooks, coming soon) and any updates to the material via Github

• A pre-order for the full book once it is complete

You can order now - You will be the first to receive the new material when it launches 😋 

What's The Plan?

• [NEW] Online Version Available to Book Readers

  • My roadmap for the book:

• The Biostatistics Group—New Partnership Alert!

• The Biostatistics Group Is Looking For Writers and …

• I’m On Youtube And Spotify!

• [DISCORD] Causal Inference🤓 Journal + Book Club

  • Journal Club #4 — Recap

• [COURSE] Introduction to Biostatistics — Exclusive …

• Consulting—Training and Workshops

• Other (FREE) Resources You Might Like

The Biostatistics Group—New Partnership Alert!

The Biostatistics Group is proud to announce our partnership with Quantfish, who provide expert statistics training for health & social scientists.

Courses taught by world-leading methodologists in: Causal inference, statistics with R, data visualization, Structural Equation Modelling, and more.

The Biostatistics Group Is Looking For Writers and Partners

1. Become a writer. 

   • _{What’s in it for you?}

     • _{You showcase your expertise, including a short author profile and a link of your choice.}

     • _{Our website gets 2k+ clicks a month—this is a great way to drive traffic to a link of your choice and we share your article on Linkedin, where you can expect anywhere from 30 to 500 clicks, depending on the topic.}

2. Become an official partner - _{Career experts, educational orientation services, product-vendors in the biostatistics space, consultants, educational content creators, and more.} 

   • _{What’s in if for you?}

     • _{We write a blog about your offerings on our website (expect around 30-200 clicks in the first week). This also creates a backlink to a fast growing website (over 10x traffic in under a year).}

     • _{We drive leads to your services.}

Check out our new partnerships with Adoc Talent Management, Easy-Statistik, AInovate, and JB Statistical Consulting!

Interested? Email admin@biostatistics.ca or send me a DM!

I’m On Youtube And Spotify!

All my new video content will be on Youtube

Listen to my podcast with Dr. Christian Geiser, a Structural Equation Model expert and educator at Quantfish, an amazing e-learning platform.

Expect exclusive Youtube and Spotify content in the future.

[DISCORD] Causal Inference🤓 Journal + Book Club

Journal Club #4 — Recap

Andy talked about the credence framework, a method leveraging a modern AI technique, namely auto variational encoding, to generate synthetic data that captures the complexities and subtleties of real-world data.

This method promises to provide more effective benchmarking for modern causal super-tools such as TMLE, DML, and more.

🙏 Thanks to everyone who joined the conversation. Next month, Katherine Davis will be leading Journal Club #5, stay tuned!

Once in the Discord ➡️ voice channels ➡️ journal-club or book-club, the events will be held there!

The community hosts causal AI founders looking for co-founders, job postings, clubs, memes, and other exclusive community features—all free!

[COURSE] Introduction to Biostatistics — Exclusive Discount

Aleksander Molak and I have finished the Founding Cohort.

We’re currently working hard on creating the material for the self-paced version that will be available online. What do you think about my setup?

Recording the Intro to Biostats course!

We will offer an exclusive discount to those who join the waiting list, as a token of appreciation for their patience.

Consulting—Training and Workshops

You may already know that I’m a University Lecturer and that my passion is teaching 💜 

I offer training and workshops in causal inference, real-world evidence, and biostatistics.

I’ve trained

• Pharma RWE teams

• Industry scientists in biotech

• Research Labs

My approach is engaging, warm, and thorough.

Ready to discuss how I can help?

I’m almost fully-booked, but could take on an additional workshop or training.

Other (FREE) Resources You Might Like

Causal Inference Nerds 🤓 Discord Community

Q&A Forum, Meme channel, Office hours voice channel, Journal Club, and more!

https://discord.gg/2xKvHMhYDg

biostatistics.ca Newsletter!

Stay at the forefront of biostatistics and industry with our brand new newsletter

https://www.biostatistics.ca/newsletter/

FREE Github Library of Code Notebooks

We created a free Github repo with all our code notebooks, check it out!

https://www.biostatistics.ca/github-library/

Causal Inference Linkedin Group

Join 3.3k+ Causal Inference Enthusiasts

www.biostatistics.ca/the-biostatistics-roadmap-or-how-to-become-a-great-biostatistician

Dear {{first_name|Causal Inference in Biostatistics Enthusiast}},

Last month, I took a break from the newsletter, and I missed it! We’re back now with a lot of exciting news and updates.

Are you ready? Let’s go!

[BOOK] Online Version Coming Soon 🥳

UPDATE: I’ve upgraded my book quite a bit. To all those who preordered, expect an online version soon, to go along with the pdf! The physical version will come after writing the whole thing.

I’m currently wrapping up Chapter 4 — Removing Confounding and Propensity Score Methods—, Chapter 5 —The Art of Observational Studies: Pragmatically Bridging Causal Frameworks—, and Chapter 7—Quasi-Experimental Designs: Simulated Randomness.

Check this out:

Chapter 4 Case-Study: a famous study of Right Hearth Catheterization. The original study did not visualize the data and used outdated Propensity Score Methods — we will remedy this together!

The case-study code-notebooks in these Chapters are easily the best I wrote so far!

If you pre-order my Causal Inference in Statistics, with Exercises, Practice Projects, and R/Python Code Notebooks, you now receive

• A 158 page PDF with the first 3 chapters

• 5 R code notebooks

• Receive the new parts of the book upon release (3 new chapters + 6 additional notebooks, coming soon) and any updates to the material via Github

• A pre-order for the full book once it is complete

You can order now - You will be the first to receive the new material when it launches 😋 

What's The Plan?

• [BOOK] Online Version Coming Soon 🥳

• FRESH MEME ALERT

• [CODE NOTEBOOK] New Blog on Welch’s T-Test

• @_thestatsnerd Is Back on Youtube!

• [DISCORD] Causal Inference🤓 Journal + Book Club

  • Journal Club #3 — Recap

• [COURSE] Introduction to Biostatistics — Exclusive …

• Best Regression Books with Reviews | The Full List

• Consulting—Training and Workshops

• Other (FREE) Resources You Might Like

FRESH MEME ALERT

We all know that feeling 😅

[CODE NOTEBOOK] New Blog on Welch’s T-Test

Welch’s T-test is the default in R.

I advise to NOT perform Levene’s test for homogeneity of variance to decide if a standard t-test or a Welch’s t-test for unequal variances should be applied.

I’ve written a short blog and am giving you a free code notebook to try for yourself—check it out!

@_thestatsnerd Is Back on Youtube!

All my new video content will be on Youtube

Listen to my podcast with Dr. Christian Geiser, a Structural Equation Model expert and educator at Quantfish, an amazing e-learning platform.

Expect exclusive Youtube content in the future.

[DISCORD] Causal Inference🤓 Journal + Book Club

Whether you're a student, researcher, practitioner, or just curious, this is a great place to:

• 📖 Discuss important papers or books

• 💬 Ask and answer questions

• 🧪 Share resources and code

• 🌍 Connect with like-minded peers

Journal Club #3 — Recap

Our discussion emphasized how DAGs (causal diagrams) help clarify competing causal explanations for links between Group B Streptococcus colonization, invasive disease, and preterm birth.

The discussion highlighted a central causal inference challenge: we can’t resolve competing DAGs with observational data alone—RCTs and well-designed longitudinal studies will be crucial.

🙏 Thanks to everyone who joined the conversation. Next month, Andy Wilson will be leading Journal Club #4, stay tuned!

Once in the Discord ➡️ voice channels ➡️ journal-club or book-club, the events will be held there!

[COURSE] Introduction to Biostatistics — Exclusive Discount

Aleksander Molak and I have finished the Founding Cohort.

We’re currently working hard on creating the material for the self-paced version that will be available online.

We will offer an exclusive discount to those who join the waiting list, as a token of appreciation for their patience.

Best Regression Books with Reviews | The Full List

Regression models are the backbone of empirical work.

Fundamentals, Bayesian approaches, Multilevel/Hierarchical/Mixed models, Survival analysis, GAM, quantile regression, GLM, and more

We got you covered!

Consulting—Training and Workshops

You may already know that I’m a University Lecturer and that my passion is teaching 💜 

I offer training and workshops in causal inference, real-world evidence, and biostatistics.

I’ve trained

• Pharma RWE teams

• Industry scientists in biotech

• Research Labs

My approach is engaging, warm, and thorough.

Ready to discuss how I can help?

Other (FREE) Resources You Might Like

Causal Inference Nerds 🤓 Discord Community

Q&A Forum, Meme channel, Office hours voice channel, Journal Club, and more!

https://discord.gg/2xKvHMhYDg

biostatistics.ca Newsletter!

Stay at the forefront of biostatistics and industry with our brand new newsletter

https://www.biostatistics.ca/newsletter/

FREE Github Library of Code Notebooks

We created a free Github repo with all our code notebooks, check it out!

https://www.biostatistics.ca/github-library/

Causal Inference Linkedin Group

Join 3.3k+ Causal Inference Enthusiasts

www.biostatistics.ca/the-biostatistics-roadmap-or-how-to-become-a-great-biostatistician

Dear {{first_name|Causal Inference in Biostatistics Enthusiast}},

[BOOK] Three (3) Chapters from my Causal Inference Book Available Soon—including case-study code notebooks

I’m currently wrapping up Chapter 4 — Removing Confounding and Propensity Score Methods—, Chapter 5 —The Art of Observational Studies: Pragmatically Bridging Causal Frameworks—, and Chapter 7—Quasi-Experimental Designs: Simulated Randomness.

Check this out:

Chapter 4 Case-Study: a famous study of Right Hearth Catheterization. The original study did not visualize the data and used outdated Propensity Score Methods — we will remedy this together!

The case-study code-notebooks in these Chapters are easily the best I wrote so far!

If you pre-order my Causal Inference in Statistics, with Exercises, Practice Projects, and R Code Notebooks, you now receive

• A 158 page PDF with the first 3 chapters

• 5 R code notebooks

• Receive the new parts of the book upon release (3 new chapters + 6 additional notebooks, coming soon) and any updates to the material via Github

• A pre-order for the full book once it is complete

You can order now - You will be the first to receive the new material when it launches 😋 

What's The Plan?

• [BOOK] Three (3) Chapters from my Causal Inference …

• [LIVE Event] Structure Meets Causality: Structural …

  • Journal Club #2

  • Book Club #1

• [COURSE] Introduction to Biostatistics — Exclusive …

• [Case-Study] Don’t Drop Non-Significant Interactio …

• biostatistics.ca Launched a Newsletter and a FREE …

• I Featured On A Podcast!

• Consulting

• Other (FREE) Resources You Might Like

[LIVE Event] Structure Meets Causality: Structural Equation Models, A Deep Dive

Dr. Christian Geiser is a world-renown leader of Causality in SEM, and runs a successful statistics education platform Quantfish.

What You’ll Learn:

• How causality and SEM are related, common myths about SEM, and more.

• Tools and resources, for beginners and pros, about Causality and SEM

• Your questions answered LIVE!

  • _{Have a question for us? Send it in now by replying to this message, and we will feature it live!}

When? July 16, 2025 🕛 12 PM Eastern Time
Who? Speakers: Justin Belair & Dr. Christian Geiser
How? Click below to reserve your spot!

[DISCORD] Causal Inference Journal Club + Book Club 🤓 

Whether you're a student, researcher, practitioner, or just curious, this is a great place to:

• 📖 Discuss important papers or books

• 💬 Ask and answer questions

• 🧪 Share resources and code

• 🌍 Connect with like-minded peers

Journal Club #2

When: July 9, 10:30AM Eastern Time
Topic: Can we trust observational data? Keeping bias in mind

Book Club #1

When: July 23, 11:00AM Eastern Time
Topic: The Art of Statistics, by David Spieghalter

Once in the Discord ➡️ voice channels ➡️ journal-club or book-club, the events will be held there!

[COURSE] Introduction to Biostatistics — Exclusive Discount

Aleksander Molak and I have finished the Founding Cohort. They gave us amazing feedback and insights into making this course exceptional. We are working to produce the self-paced online version as you read this! Our students loved it, and this touches our hearts 💜

[Case-Study] Don’t Drop Non-Significant Interactions From Your Model

Recently, a client suggested we just drop non-significant interactions from the model, despite the split-plot design naturally flowing into a model of the interactions. Below is how I responded. The client agreed, and we used a better and more statistically sound approach.

If you need help with statistics, reach out for a free 30-minute discovery call.

Chapter 9 of my upcoming book will be all about interpretation of interaction (or moderation) effects, and mediation analysis—two topics that benefited tremendously from our advanced understanding of causality.

biostatistics.ca Launched a Newsletter and a FREE Github library with code

We’re expanding out content: to weekly articles, we now added a newsletter and free repository to share code notebooks!

I Featured On A Podcast!

Thanks to Venkat Raman from Aryma labs for inviting me on his great Casual Causal Talk podcast — we discussed how I got started with Causal Inference, schools of thought in Causal Inference, Predictions vs Causality, Causal Discovery vs Inference, Causality in LLMs, and more.

Oh, and by the way, I’m reviving my Youtube Channel. There, you can find my live talks with Robert Rachford (x2), Darko Medin (x2), Mark Anderson, stats tutorials, and more—check it out!

biostatistics.ca Collaborations and Partnerships

1. Become a writer. 

   • _{What’s in it for you?}

     • _{You showcase your expertise, including a short author profile and a link of your choice.}

     • _{Our website gets 1k+ clicks a month—this is a great way to drive traffic to a link of your choice and we share your article on Linkedin, where you can expect anywhere from 30 to 200 clicks, depending on the topic.}

2. Become an official partner - _{Career experts, educational orientation services, product-vendors in the biostatistics space, consultants, educational content creators, and more.} 

   • _{What’s in if for you?}

     • _{We write a blog about your offerings on our website (expect around 30-200 clicks in the first week). This also creates a backlink to a fast growing website (over 10x traffic in under a year).}

     • _{We drive leads to your services.}

Check out our new partnerships with Adoc Talent Management, Easy-Statistik, AInovate, MarketEdge, and JB Statistical Consulting!

Interested? Email admin@biostatistics.ca or send me a DM!

Consulting

Here's what a Lead R&D scientist in biotech, Victor Kang, whom I’ve collaborated with for over a year had to say:

" [...] Immediately from our first call, Justin stood out from other candidates that I have considered: he was highly knowledgeable about the underlying theory behind various statistical approaches, and he could clearly explain when and why I should use them. Furthermore, he was unique in appreciating the need to balance academic rigour with the realities of running biological field trials (i.e., uncontrollable variables, time, cost).[...] I have learned much theory and practice from him, and our collaborations have resulted in real business impact. [...] I look forward to continuing our collaboration!"

Ready to discuss how I can help?

Other (FREE) Resources You Might Like

Causal Inference Nerds 🤓 Discord Community

Q&A Forum, Meme channel, Office hours voice channel, Journal Club, and more!

https://discord.gg/2xKvHMhYDg

biostatistics.ca Newsletter!

Stay at the forefront of biostatistics and industry with our brand new newsletter

https://www.biostatistics.ca/newsletter/

FREE Github Library of Code Notebooks

We created a free Github repo with all our code notebooks, check it out!

https://www.biostatistics.ca/github-library/

Causal Inference Linkedin Group

Join 3k+ Causal Inference Enthusiasts

www.biostatistics.ca/the-biostatistics-roadmap-or-how-to-become-a-great-biostatistician

Dear {{first_name|Causal Inference in Biostatistics Enthusiast}},

Two Chapters from my Causal Inference Book Available Soon (including case-study code notebooks)

I’m currently wrapping up writing the Observational Studies and the Quasi-Experiments Chapters.

Check this out:

Sweet Plot for my Regression Discontinuity Design (RDD) Case-Study

The case-study code-notebooks in these Chapters are easily the best I wrote so far!

If you pre-order my Causal Inference in Statistics, with Exercises, Practice Projects, and R Code Notebooks, you now receive

• A 158 page PDF with the first 3 chapters

• 5 R code notebooks

• Continuous updates to the material

• Receive the new parts of the book upon release (2 new chapter + 4 additional notebooks)

• A pre-order for the full book once it is complete

You can order now - You will be the first to receive the new material when it launches 😋 

What's The Plan?

• Two Chapters from my Causal Inference Book Availab …

• New Causal Inference Nerds Journal Club - 1st Edit …

• Introduction to Biostatistics Online Course

• Marginal Effects - What the $%#@

• biostatistics.ca Launched a Newsletter and a FREE …

• Time-to-event (or Survival) Analysis, 8+ Tutorials …

• biostatistics.ca Collaborations and Partnerships

• My Experience in Consulting

• Other (FREE) Resources You Might Like

New Causal Inference Nerds Journal Club - 1st Edition! 🤓 

Whether you're a student, researcher, practitioner, or just curious, this is a great place to:

• 📖 Discuss recent papers

• 💬 Ask and answer questions

• 🧪 Share resources and code

• 🌍 Connect with like-minded peers

***Our first edition will be held TOMORROW on June 4, 1:30PM Eastern Time***

📗 Topic: "relative ignorability", which describes when nonignorable missingness doesn't matter for your data. We will explore this phenomenon in the context of unobserved confounding, which results in nonignorable missingness.

All levels welcome, from beginners to experts.

Once in the discord ➡️ voice channels ➡️ journal-club, the event will be held there!

Introduction to Biostatistics Online Course

I’ve teamed up with the great Aleksander Molak, author of Causal Inference and Discovery in Python, to deliver my first online course, Intro to Biostats, and we are going through our first Founding Cohort.

We’re almost done with the founding cohort - the self-paced version of the course will then be available for everybody!

In case you missed it, a sample of the material is available for free!

Marginal Effects - What the $%#@

If you use statistical models for inference (e.g. fitting a regression and interpreting p-values and confidence intervals) and you don't know how tu use and interpret Marginal Effects, you're missing out on a HUGE piece of knowledge.

I still can't believe it exists, and it's free!

biostatistics.ca Launched a Newsletter and a FREE Github library with code

We’re expanding out content: to weekly articles, we now added a newsletter and free repository to share code notebooks!

Time-to-event (or Survival) Analysis, 8+ Tutorials with code!

We are launching an extensive series of tutorials with code with our collaborator Shishir Rao on Survival Analysis.

Shishir has a background in Engineering/Statistics. After more than a decade working in the energy industry, he is currently exploring independent consulting in the field of reliability analytics.

His blog post series is amazing - it’s basically a full intro course on survival analysis, with code and everything!

biostatistics.ca Collaborations and Partnerships

Two ways you can help the biostatistics community.

1. Become a writer. 

   • What’s in it for you? 

     • You showcase your expertise, including a short author profile and a link of your choice.

     • Our website gets 1k+ clicks a month—this is a great way to drive traffic to a link of your choice

2. Become an official partner - _{Career experts, educational orientation services, product-vendors in the biostatistics space, consultants, educational content creators, and more.} 

   • What’s in if for you? 

     • We write a blog about your offerings on our website (expect around 30-75 clicks in the first week).

     • We drive leads to your services.

     • This also creates a backlink to a fast growing website (over 10x traffic in under a year).

     • It’s the best time to join us!

Interested? Email admin@biostatistics.ca or send me a DM!

Our website is growing about 10x faster than we would’ve expected - we need partners to meet the audience’s growing demands!

My Experience in Consulting

I’m currently close to being fully booked for 2025 - what a blessing! - so reach out to secure a spot if you think I can help :)

Areas of expertise:

• _{Causal Inference and Real-World Evidence}

• _{Biotech/Biopharma/Non-Clinical Research and Chemistry, Manufacturing, and Control}

• _{R&D Industry Scientists/Design of Experiments (both in the lab and in the field)}

• _{Social Sciences (psychology, sociology, economics, and more)}

• _{Biomedical Sciences}

• _{Data Visualization}

• _{Discrete Choice Experiments and Surveys}

I offer:

• _{Statistical Consulting Services}

• _{Training/Workshops}

• _{Public Speaking/Guest Lectures}

• _{Other Custom Services}

Here's what a recent R&D scientist in biotech whom I’ve collaborated with for over a year had to say:

" [...] Immediately from our first call, Justin stood out from other candidates that I have considered: he was highly knowledgeable about the underlying theory behind various statistical approaches, and he could clearly explain when and why I should use them. Furthermore, he was unique in appreciating the need to balance academic rigour with the realities of running biological field trials (i.e., uncontrollable variables, time, cost).

[...] I have learned much theory and practice from him, and our collaborations have resulted in real business impact. [...] I look forward to continuing our collaboration!"

Ready to discuss how I can help?

Other (FREE) Resources You Might Like

Causal Inference Nerds 🤓 Discord Community

Q&A Forum, Meme channel, Office hours voice channel, Journal Club, and more!

https://discord.gg/2xKvHMhYDg

biostatistics.ca Newsletter!

Stay at the forefront of biostatistics and industry with our brand new newsletter

https://www.biostatistics.ca/newsletter/

FREE Github Library of Code Notebooks

We created a free Github repo with all our code notebooks, check it out!

https://www.biostatistics.ca/github-library/

Causal Inference Linkedin Group

Join 3k+ Causal Inference Enthusiasts

www.biostatistics.ca/the-biostatistics-roadmap-or-how-to-become-a-great-biostatistician

Dear {{first_name|Causal Inference in Biostatistics Enthusiast}},

New Causal Inference Discord Community!

I finally did it! I created a discord community that already hosts over 60 members and is hosting lively discussions.

There you can find:

• Q&A forum: for structured causal inference discussions, usually more technical or detailed

• Meme channel: for laughter

• Featured-content channel: a platform to host featured content and spotlight it

• Causal-inference-in-statistics-book: a private channel for those who purchased my book and want to discuss it

• Office Hours voice channel: a private channel where I will be available at pre-specified times to discuss and answer questions. Those who purchase the book have access to it.

We’re already discussing with the community plans to create a Journal Club and a “night science” club, to discuss WILD ideas, informally.

What's Today's Plan?

1. New Causal Inference Discord Community!

2. Exciting News: Introduction to Biostatistics Onlin …

3. Causal Inference Book Is Available

4. biostatistics.ca Launched a Newsletter and a FREE …

5. Kronecker Product Use-Case (with FREE R Code)

6. Discrete Choice Experiment Case-Study

7. NEW BLOG POST SERIES : Time-to-event (or Survival) …

8. Next Live Event: Causal Inference and Epidemiology

9. ISPOR 2025 Conference in Montreal

10. biostatistics.ca Collaborations and Partnerships

11. My Experience in Consulting

12. Other (FREE) Resources You Might Like

Exciting News: Introduction to Biostatistics Online Course

I’ve teamed up with the great Aleksander Molak, author of Causal Inference and Discovery in Python, to deliver my first online course, Intro to Biostats, and we are going through our first Founding Cohort.

Our first live lectures have been amazing and the feedback has been way better than I expected!

Causal Inference Book Is Available

If you pre-order the my Causal Inference in Statistics, with Exercises, Practice Projects, and R Code Notebooks, you now receive

• A 158 page PDF with the first 3 chapters

• 4 R code notebooks

• Continuous updates to the material

• Receive the new parts of the book upon release

• A pre-order for the full book once it is complete

You can order now!

biostatistics.ca Launched a Newsletter and a FREE Github library with code

We’re expanding out content: to weekly articles, we now added a newsletter and free repository to share code notebooks!

Kronecker Product Use-Case (with FREE R Code)

What is the Kronecker product?

It is a special operation on matrices (i.e. arrays) that I recently used in my consulting practice.

Check out the tutorial below!

Discrete Choice Experiment Case-Study

I’m currently working on a DCE in patient-preference for medical treatments.

Check out the case-study below where I provided value after a 30-minute call.

I’m extremely proud of these accomplishments: My goals is to improve scientific practice everywhere!

Stay tuned as I update you on the progress of this collaboration.

NEW BLOG POST SERIES : Time-to-event (or Survival) Analysis, tutorials with code!

We are launching an extensive series of tutorials with code with our collaborator Shishir Rao on Survival Analysis.

Shishir has a background in Engineering/Statistics. After more than a decade working in the energy industry, he is currently exploring independent consulting in the field of reliability analytics.

Next Live Event: Causal Inference and Epidemiology

Who would you love to see me discuss epi/causal inference with live on Linkedin?

Reply to this email or send me a Linkedin DM to let me know!

ISPOR 2025 Conference in Montreal

From May 13-May 15, a major Health Economics and Outcomes Research conference will be held in Montreal.

Want to meet for coffee/food? Reach out by replying to this email of sending me a DM!

biostatistics.ca Collaborations and Partnerships

Two ways you can help the biostatistics community.

1. Become a writer. 

   • What’s in it for you? 

     • You showcase your expertise, including a short author profile and a link of your choice.

     • Our website gets 1k+ clicks a month—this is a great way to drive traffic to a link of your choice

2. Become an official partner.

   • We’re expanding our partnerships with, including but not limited to, career experts, educational orientation services, product-vendors in the biostatistics space, consultants, educational content creators, and more.

   • What’s in if for you? 

     • We write a blog about your offerings on our website (expect around 30-75 clicks in the first week).

     • We drive leads to your services.

     • This also creates a backlink to a fast growing website (over 10x traffic in under a year).

     • It’s the best time to join us!

Interested? Email admin@biostatistics.ca or send me a DM!

Our website is growing about 10x faster than we would’ve expected - we need partners to meet the audience’s growing demands!

My Experience in Consulting

Areas of expertise:

• Causal Inference and Real-World Evidence

• Biotech/Biopharma/Non-Clinical Research and Chemistry, Manufacturing, and Control

• R&D Industry Scientists/Design of Experiments (both in the lab and in the field)

• Social Sciences (psychology, sociology, economics, and more)

• Biomedical Sciences

• Data Visualization

• Discrete Choice Experiments and Surveys

I offer:

• Statistical Consulting Services

• Training/Workshops

• Public Speaking/Guest Lectures

• Other Custom Services

If you think I can help, I offer a free 30 minute consultation!

Here's what a recent R&D scientist in biotech whom I’ve collaborated with for over a year had to say:

" [...] Immediately from our first call, Justin stood out from other candidates that I have considered: he was highly knowledgeable about the underlying theory behind various statistical approaches, and he could clearly explain when and why I should use them. Furthermore, he was unique in appreciating the need to balance academic rigour with the realities of running biological field trials (i.e., uncontrollable variables, time, cost).

[...] I have learned much theory and practice from him, and our collaborations have resulted in real business impact. [...] I look forward to continuing our collaboration!"

Did you know I offer comprehensive data science and analytics services? From strategic data solutions to advanced predictive modeling, I can support your team in unlocking the potential of your data.

Ready to discuss how I can help?

Other (FREE) Resources You Might Like

Causal Inference Nerds 🤓 Discord Community

Q&A Forum, Meme channel, Office hours voice channel, Journal Club, and more!

https://discord.gg/2xKvHMhYDg

biostatistics.ca Newsletter!

Stay at the forefront of biostatistics and industry with our brand new newsletter

https://www.biostatistics.ca/newsletter/

FREE Github Library of Code Notebooks

We created a free Github repo with all our code notebooks, check it out!

https://www.biostatistics.ca/github-library/

The Best Causal Inference Guide Out There: Books, Courses, and More

I’m often asked where to start learning causal inference…look no further!

https://www.biostatistics.ca/causal-inference-guide-books-courses-and-more/

Causal Inference Linkedin Group

Join 2.8k+ Causal Inference Enthusiasts

www.biostatistics.ca/the-biostatistics-roadmap-or-how-to-become-a-great-biostatistician

I Featured on A Podcast!

I talked about my Causal Inference in Statistics book on The Effective Statistician Podcast

https://podcastae8fac.podigee.io/421-new-episode

Dear {{first_name|Causal Inference in Biostatistics Enthusiast}},

Design of Experiments (DOE) by a Leading Expert

Mark Anderson is a chemical engineer that came to statistics by a non-traditional route—something that many can relate to.

He now has decades of experience designing experiments and has wrote a many books on the subject—a testament to his professional growth and successful career.

What You’ll Learn:

• How industries like pharma, manufacturing & engineering use DOE to save time & money

• The role of AI in experimental design (and its limits)

• Must-read books: Mark’s DOE, RSM & Formulation Simplified trilogy

• Your questions answered LIVE!

• When? April 23, 2025 🕛 12 PM Eastern Time

• Who? Speakers: Justin Belair & Mark Anderson

• How? Click below to reserve your spot!

BONUS: Mark will also share details on the upcoming Stat-Ease 2025 Online DOE Summit (June 16-20)—a must-attend for experimenters worldwide!

Have a question for Mark? Send it in now by replying to this message, and we will feature it live!

If you want to learn more about Design of Experiments (DOE), the team at biostatistics[.]ca has prepared a full list of DOE book reviews for you to enjoy.

What's Today's Plan?

• Design of Experiments (DOE) by a Leading Expert

• Exciting News: Introduction to Biostatistics Onlin …

• New Services Available!

• The Most Exciting Projects Of 2025 So Far For bios …

• Other (FREE) Resources You Might Like

Exciting News: Introduction to Biostatistics Online Course

I’ve teamed up with the great Aleksander Molak, author of Causal Inference and Discovery in Python, to deliver my first online course, Intro to Biostats, and we are going through our first Founding Cohort.

Our first live lectures have been amazing and the feedback has been way better than I expected!

New Services Available!

In additional to full scientific consulting services (study design, data management and analysis, manuscript writing, etc.), I now offer custom services!

• Training

• Workshops

• Public Speaking

• Guest Lectures

• Other Custom Services

If you think I can help, I offer a free 30 minute consultation!

Did you know I offer comprehensive data science and analytics services? From strategic data solutions to advanced predictive modeling, I can support your team in unlocking the potential of your data.

Ready to discuss how I can help?

The Most Exciting Projects Of 2025 So Far For biostatistics.ca

Some projects we are working on at biostatistics.ca:

• NEW custom Workshops and Training services that are tailored to your unique needs

• A handful of partnerships that promise to better serve your needs

• A causal inference space that will be the best collection of causal inference resources out there

• A weekly biostatistics.ca newsletter

• Launching our brand: The Biostatistics Group

• and more…

Other (FREE) Resources You Might Like

The Best Causal Inference Guide Out There: Books, Courses, and More

I’m often asked where to start learning causal inference…look no further!

https://www.biostatistics.ca/causal-inference-guide-books-courses-and-more/

The Best Epidemiology Books | The Full List

No matter what your level of epidemiology, check out this full list of book reviews!

https://www.biostatistics.ca/the-best-epidemiology-books-the-full-list/

The Best Design of Experiments Books | The Full List

DoE is a fascinating subject that helps experiments run efficiently, saving time and money, and strengthening results. Check out this full list of book reviews!

https://www.biostatistics.ca/best-design-of-experiments-books-the-full-list/

Causal Inference Linkedin Group

Join 1.8k+ Causal Inference Enthusiasts

www.biostatistics.ca/the-biostatistics-roadmap-or-how-to-become-a-great-biostatistician

I Featured on A Podcast!

I talked about my Causal Inference in Statistics book on The Effective Statistician Podcast

https://podcastae8fac.podigee.io/421-new-episode

The Complete Biostatistics Roadmap, or How to Become a Great Biostatistician (with Books and Resources) - biostatistics.ca

I’m often asked by budding biostatisticians curious and eager to learn more to advise them on a developmental roadmap. Here is what I think you need to become a great applied biostatistician, which in itself is more about the journey than the destination…

www.biostatistics.ca/the-biostatistics-roadmap-or-how-to-become-a-great-biostatistician

Dear {{first_name|Causal Inference in Biostatistics Enthusiast}},

I’m excited to share with you my upcoming involvement in the scientific community.

Get Your Writing Promoted For Free To A Huge Niche Audience

Last year, many great collaborations were born on biostatistics.ca. We are seeking new writing collaborations:

• Technical Tutorials

• Case-Studies

• Career Testimonials

• And more…

What’s the deal?

You work with our editing team to write a blog post and we promote it to our large niche audience:

• +2100 followers on Linkedin Page

• +17500 followers on Linkedin Profile

• +700 organic visits per month

It’s a great way to tap into the biostatistics community!

What's Today's Plan?

• Get Your Writing Promoted For Free To A Huge Niche …

• The Best DA(n)G Blog Post On The Internet (of cour …

• My Causal Inference Book Is Finally Coming To Life

• My Intro To Biostats Course Just Got A Major Boost

• I’m Biased And I Think My Work Is Great (I Need Yo …

• Statistics Is Painful, I Know. But It Doesn’t Have …

• The Most Exciting Project Of 2025 So Far

• 1-Hour Of Free Causal Inference Content (Please, D …

• Other (FREE) Resources You Might Like

Meet (Or Avoid) Me At ISPOR 2025 Conference in Montreal

Meet with the biostatistics.ca team at ISPOR, a leading conference of Health Economics and Outcomes Research.

It’s in my hometown of Montreal! So if you want to schedule a coffee chat at one of my favorite cafés, don’t hesitate to reach out by replying to this email :)

_{**I’ve been having only one coffee a day for the past month and a half (incredible, I know), but I can make an exception for some sweet stats chats ;)}

The Best DA(n)G Blog Post On The Internet (of course it’s the best, it’s mine)

Directed Acyclic Graphs (DAGs, an acronym that allows me the cheap wordplay you see above) are intuitive visual tools to help with causal inference.

In my latest blogpost, we forego technical definitions and opt instead for concrete examples with data, to help highlight:

• Confounding bias

• Collider (i.e. selection) bias

• Mediation Analysis

My Causal Inference Book Is Finally Coming To Life

I’m wrapping up the final touches for Part I of Causal Inference in Statistics, with Exercises, Practice Projects, and R Code Notebooks! It will be 4 chapters and over 200 pages, including 3 case-studies complete with datasets and code notebooks.

Those that pre-ordered should expect it soon.

In the meantime, check out my First Chapter.

_{** I was expecting it to come faster, but with only one coffee a day (see above), things have been slower than expected!}

My Intro To Biostats Course Just Got A Major Boost

I’m currently negotiating an arrangement with a superb partner to help bring the course to the next level (you already know him, and if you don’t, you will regret not knowing him before!)

Thanks to everybody for the patience as we iron out the final details before I make an announcement.

Expect to hear from me in the next few weeks.

The best way to stay updated on my progress is through this newsletter and Linkedin.

I’m Biased And I Think My Work Is Great (I Need Your Help To Tell Me It’s Not)

If you’re interested in reviewing my book and giving me feedback, reach out by replying to this email or at belairjustin@gmail.com.

Statistics Is Painful, I Know. But It Doesn’t Have To Be!

It is no secret anymore that research these days needs to be grounded in causal inference principles.

I can help and I offer a free 30 minute consultation!

Did you know I offer comprehensive data science and analytics services? From strategic data solutions to advanced predictive modeling, I can support your team in unlocking the potential of your data.

Ready to discuss how I can help?

The Most Exciting Project Of 2025 So Far

Some projects we are working on at biostatistics.ca:

• NEW custom Workshops and Training services that are tailored to your unique needs

• A handful of partnerships that promises to better serve your needs

• A causal inference space that will be the best collection of causal inference resources out there

• A Weekly Newsletter

• Attending the ISPOR 2025 conference in Montreal

• and more…

1-Hour Of Free Causal Inference Content (Please, Don’t Miss Out!)

Last month, Darko Medin and I did our second edition of the Causal Inference podcast. A lot of great questions were asked.

We will share clips from the recording every week for the next few weeks on Linkedin!

Other (FREE) Resources You Might Like

The Best Causal Inference Guide Out There: Books, Courses, and More

I’m often asked where to start learning causal inference…look no further!

https://www.biostatistics.ca/causal-inference-guide-books-courses-and-more/

The Complete Biostatistics Roadmap, or How to Become a Great Biostatistician (with Books and Resources) - biostatistics.ca

I’m often asked by budding biostatisticians curious and eager to learn more to advise them on a developmental roadmap. Here is what I think you need to become a great applied biostatistician, which in itself is more about the journey than the destination…

www.biostatistics.ca/the-biostatistics-roadmap-or-how-to-become-a-great-biostatistician

Causal Inference Linkedin Group

Join 1.8k+ Causal Inference Enthusiasts

www.biostatistics.ca/the-biostatistics-roadmap-or-how-to-become-a-great-biostatistician

Dear {{first_name|Causal Inference in Statistics Enthusiast}},

We’re Announcing A New Linkedin Live Podcast!

What? Causal Inference II

• Selected Topics in Causal Inference

• Live Q&A Session

• Recommendations to enhance your causal inference expertise or begin your learning journey

• Please, feel free to propose any topics you would like us to cover by responding to this email directly or reaching out on Likedin

When? Wednesday, February 12, 10-11AM EDT

Who? Darko Medin, an internationally-known expert in biostatistics and Causal Inference and, yours truly, Justin Belair of JB Statistical Consulting

Don’t miss the opportunity to ask any questions live, we answer everybody (and it’s my favorite part 😉)

Now, what’s in store for this edition of the newsletter, anyways!? Buckle up, it’s a great one.

Table of Contents

• We’re Announcing A New Linkedin Live Podcast!

• New Causal Inference Blog Post With (FREE) Downloa …

• Book Writing Updates

  • I Need Your Help!

  • Sneak-Peak At My Book ;)

  • A Sincere Thank You To All Those Who Support My Wr …

• 🎁5 Signs You Need Causal Inference In Your Analys …

• Online Course Updates

• Updates for biostatistics.ca

  • Writing Collaborations ✍️

  • Partnerships 🤝

• Causal Inference Discord Community?

• Need Help With Stats?

• Life Updates

• Other (FREE) Resources You Might Like

New Causal Inference Blog Post With (FREE) Downloadable Code Notebook

Check out the preview image below or click here to read it and/or access the code notebook for FREE.

Click Here To Read The Blog Post

Book Writing Updates

I’m rounding up Part I of the book, which contains Chapters 1-4 and is about 250 pages.

I wanted to release this on January 31st, but a lot of things happened in my personal life. I’m working hard to deliver a quality product!

Thank you all for your patience ❤️

Oh, and by the way… the case-study of Chapter 4 with Real-World Data and step-by-step code instructions is going to be amazing, I feel it! 🔥

I Need Your Help!

I’m looking for real-world datasets that can be used to showcase causal methods in case-studies.

Have any suggestions or would like to help?

I believe this can be a win-win.

• If you have a case-study you can share, I will use it in my book.

• It helps me by making my book more compelling by showing real-world use cases to the reader.

• It helps you by showing the world the sort of technologies you are developing, the problems you are working on, etc. and obviously there could be references to your profile, your website, papers, or anything else.

Sneak-Peak At My Book ;)

By the way, the first Chapter is still available to download for free—isn’t that cool?

**Psst, I reworked the first Chapter so those who pre-ordered will get a surprise 😉

A Sincere Thank You To All Those Who Support My Writing 🙏

If you want to support me while I work on my book, you can join hundreds of other causal inference enthusiasts and pre-order it at the early-bird ultra-discounted price 🥳

The price will consistently increase as I progress in writing the book.

Expect the final retail price for the physical copy to be around 125-200$.

The best way to stay updated on my writing progress is through this newsletter and Linkedin.

🎁5 Signs You Need Causal Inference In Your Analysis

Check them out!

Online Course Updates

I can’t say much right now because things are in the work, but I have BIG NEWS for all those that pre-ordered and those that are looking forward to my online course, Introduction to Biostatistics.

Oh, and I’m also working on a second course, but I can’t say too much right now…🤐 I’m very confident you’ll like it!

Updates for biostatistics.ca

It is quickly becoming a guiding light in biostatistics!

The response has been overwhelmingly positive and the growth metrics are honestly crazy—we would’ve never expected to be at this stage so early in the process!

Expect more great weekly articles and guides and a new causal inference space launching soon, stay tuned!

Writing Collaborations ✍️

We’re always seeking out great collaborators to share their personal testimonials, case-studies, or technical tutorials in statistics.

What's in it for you?

A chance to polish your writing skills, join the community, grow your network, and work alongside a recognized brand in the field.

Partnerships 🤝

We're seeking to establish strategic partnerships to better serve our audience: brands, admission experts, career counselors, and headhunters to support our audience.

What's in it for you?

A rapidly growing and deeply engaged audience (both through organic search and through Linkedin) with very specific and niche needs that you can help address with services you already provide.

Causal Inference Discord Community?

Members of the Causal Inference Linkedin Group have expressed interest in having a Discord community to foster exchange and ideas.

Interested in helping me put this in place? Please reach out!

Need Help With Stats?

I don’t have much space left in my calendar, but for the right project, we can make it work!

I can help with anything statistics/data/causal inference related.

I also offer custom training and workshops.

I’ve been teaching this stuff in 2 universities for over 7 years now—teaching is my true passion!

Check out my new Causal Inference Consulting page. My hope is that it will be clearer for those who don’t know me yet that I offer this expertise!

Did you know I offer comprehensive data science and analytics services (consulting and training) in statistics and causal inference?

Ready to discuss how I can help?

Life Updates

The start to 2025 has been great! I spent January in Mexico working hard on writing my book.

It was better to work in the warm Sun ☀️ than in the extreme Montreal cold this time of year (-30°C) the other day 🥶

I’ve had a lot of time to reflect on challenges and successes both in my personal life and in my career. I’m back with a new perspective and a fresh sense of purpose…

I have a feeling that this year will be special and that we’re building some amazing things together!

Until next time.

Other (FREE) Resources You Might Like

Causal Inference Linkedin Group

Join 1.6k+ Causal Inference Enthusiasts

www.biostatistics.ca/the-biostatistics-roadmap-or-how-to-become-a-great-biostatistician

The Complete Biostatistics Roadmap, or How to Become a Great Biostatistician (with Books and Resources) - biostatistics.ca

I’m often asked by budding biostatisticians curious and eager to learn more to advise them on a developmental roadmap. Here is what I think you need to become a great applied biostatistician, which in itself is more about the journey than the destination…

www.biostatistics.ca/the-biostatistics-roadmap-or-how-to-become-a-great-biostatistician

Dear {{first_name|Causal Inference in Statistics Enthusiast}},

First, book writing updates.

Chapter 3 on Graphical Causal Models is pretty much done! I still have to create a few plots and finish writing an exercise. On the good days (i.e. those that are not filled with crippling self-doubt), I am filled with pride from what I’ve accomplished in this Chapter. Check out the preview.

Causal Inference Book Chapter 3 Preview

If you like the preview, please consider pre-ordering, as it supports my writing!

215.17 KB • PDF File

Download

During the holiday season, I developed tendinitis in my wrist which forced to me stop writing for longer than I wanted. This was a blessing in disguise. Remember last month I told you that I wasn’t sure how to approach Chapter 4 on Observational Studies? The time off allowed me to read a lot more (which is how I get all my inspiration), giving me a refreshed sense of clarity on what Chapter 4 would seek to accomplish. Here’s a tentative outline.

Chapter 4 : Observational Studies

• Observing Causality or How To Approximate the Experimental Ideal

  • The Problem of Identification: Two Philosophies

  • Target Trial Emulation

  • The Relationship Between the Backdoor Criterion and Ignorability Conditions (or exchangeability)

  • The Role of Randomization

  • Potential Outcomes As Counterfactuals in Structural Causal Models

• Adjustment as Conditional Expectation: Regression Adjustment

• Adjustment as Conditional Expectation: Matching, Stratification, and Weighting Methods

• Sensitivity Analysis

• Study Designs Widely Used in Epidemiology

  • Cohort Studies

  • Case-Control Studies

  • Negative Controls

• Reporting

• Case-Study

• Bibliographical Notes

• Exercises

I’m setting an aggressive deadline of January 31st to send out Part I (Chapters 1-4) to those who pre-ordered!

I Need Your Help!

I will simultaneously send out the content to be revised and edited by those who generously offered to help with this.

If You Want To Help Reviewing My Book, Reply To This Message Or Click Here

I’m looking for real-world datasets that can be used to showcase causal methods in case-studies. Have any suggestions or would like to help?

If You Want To Help With Building Case-Studies, Reply To This Message Or Click Here

By the way, the first Chapter is already available to download for free—isn’t that cool?

Download First Chapter Free

The rest will be released in 3 parts:

• January 31, 2025 (tentative): PART I – Fundamentals of Causal Inference (Chapters 1-4, ~ 200 pages)

• Mid 2025: PART II – The Causal Inference Toolkit (Chapters 5-9, ~ 200 pages)

• End 2025: PART III – Advanced Causal Inference (Chapters 10-12, ~ 150 pages)

If you want to support me while I work on my book, you can pre-order it at the early-bird ultra-discounted price. The price will consistently increase as I progress in writing the book. Expect the final retail price for the physical copy to be around 125-200$.

Pre-Order My Book Now

Don’t worry, I’ll pay a cover designer before publishing the final version ;)

The best way to stay updated on my writing progress is through this newsletter and Linkedin.

So, What's The Plan For This Edition Of The Newsletter Anyway?

• What’s Ahead For biostatistics[.]ca In 2025

• Causal Inference Discord Community

• Linkedin Live Podcasts

• Need Help With Stats? Please, Reach Out :)

  • Other (FREE) Resources You Might Like

Need Help With Stats? Please, Reach Out :)

I’m filling out my calendar for 2025!

I can help with:

• Study Design and Statistical Analysis Plan

• Data Collection and Cleaning

• Power Calculation and Sample Size Determination

• Hypothesis Testing and Statistical Modelling

• Writing Papers, Grants, etc.

Did you know I offer comprehensive data science and analytics services? From strategic data solutions to advanced predictive modeling, I can support your team in unlocking the potential of your data.

Ready to discuss how I can help?

Book A Free 30 Minute Consultation

Other (FREE) Resources You Might Like

Causal Inference Linkedin Group

Join 1.6k+ Causal Inference Enthusiasts

www.biostatistics.ca/the-biostatistics-roadmap-or-how-to-become-a-great-biostatistician

The Complete Biostatistics Roadmap, or How to Become a Great Biostatistician (with Books and Resources) - biostatistics.ca

I’m often asked by budding biostatisticians curious and eager to learn more to advise them on a developmental roadmap. Here is what I think you need to become a great applied biostatistician, which in itself is more about the journey than the destination…

www.biostatistics.ca/the-biostatistics-roadmap-or-how-to-become-a-great-biostatistician

Dear {{first_name|Causal Inference in Biostatistics Enthusiast}},

I’m proud to announce my new book : Causal Inference in Statistics, with Exercises, Practice Projects, and R Code Notebooks!

The first Chapter is already available to download for free—isn’t that cool?

Download First Chapter Free

The rest will be released in 3 parts:

• Early 2025: PART I – Fundamentals of Causal Inference (Chapters 1-4, ~ 200 pages)

• Mid 2025: PART II – The Causal Inference Toolkit (Chapters 5-9, ~ 200 pages)

• End 2025: PART III – Advanced Causal Inference (Chapters 10-12, ~ 150 pages)

If you want to support me while I work on my book, you can pre-order it at the early-bird ultra-discounter price.

Pre-Order My Book Now

Don’t worry, I’ll pay a cover designer before publishing the final version ;)

The best way to stay updated on my writing progress is through this newsletter and Linkedin.

Book writing updates:

I’m finishing Chapter 3 on graphical causal models. It’s quite hefty, and will probably end up being around 60 pages with the code and everything.

Chapter 4 is going to be by far the most difficult Chapter to write, as I want to cover observational studies, but I’m not sure about how to do so. I want to cover classic observational designs as seen in epidemiology, but I want to also cover the counterfactual approach to causal inference as it will tie in Chapter 2 on potential outcomes with Chapter 3.

All while finding an interesting narrative thread… I first thought about discussing the debate around smoking and lung cancer, but I think it fits nicely in the intro. Maybe I’ll revisit the topic, or tackle the polio vaccine case. I’m not sure…*

Let me know what you think by replying directly to this message!

_{*One thing that is for sure is that I am going to escape Canadian winter for about a full month as I go down South to the warmer parts of the continent for the first time in my life—that is where I plan on finishing Chapter 4 and publishing Part I of the book (Chapters 1-4). I can’t wait!}

What's Today's Plan?

• What Are P-values And Why Are They So Problematic?

• Need Help With Stats? Please, Reach Out :)

• Learn More About P-Values (Reading List With Links …

• Bioequivalence Study Sample Size Calculator Based …

• Humble Brag(s)

• Revisiting Our Linkedin Live Podcast

• Other (FREE) Resources You Might Like

What Are P-values And Why Are They So Problematic?

P-values are by far the most discussed statistical topic in history (I don’t have evidence, but trust me, it’s true!)

They are often criticized, distrusted, misused, misinterpreted, and, on the flipside, used everyday in every single empirical study.

So, what are p-values and why are they so problematic?

Need Help With Stats? Please, Reach Out :)

This time of year, I’m usually fully booked, but I was able to free up some time for new and interesting research projects.

Who knows? You might be my next great collaborator—please, don’t hesitate to reach out if you think I can help.

I offer a free 30 minute consultation!

Did you know I offer comprehensive data science and analytics services? From strategic data solutions to advanced predictive modeling, I can support your team in unlocking the potential of your data.

Ready to discuss how I can help?

Learn More About P-Values (Reading List With Links)

Here's a small reading list that will make you sharp when interpreting statistical output from your favorite software 🎁 

1. Sander Greenland et al., Statistical tests, P values, confidence intervals, and power: a guide to misinterprations, 2016

   • THE resource for all p-value misinterpretations by a collection of eminent statisticians. Must be read and re-read!

2. Sander Greenland, Nonsignificance Plus High Power Does Not Imply Supper for the Null Over the Alternative, 2012

   • The title says it all...it's an easy mistake to make!

3. John M. Hoenig & Dennis M. Heisey, The Abuse of Power: The Pervasive Fallacy of Power Calculations for Data Analysis, 2001

   • Power is a widely misunderstood statistical concept. Study it!

4. Steven Goodman, A Dirty Dozen: Twelve P-Value Misconceptions, 2008

   • The title says it all.

5. Harvey J. Motulsky, Common misconceptions about data analysis and statistics, 2014

   • Goes over many misconceptions. It is quite beginner friendly, take a look!

I share more small, digestible reading lists in my Github Repo.

Check it out and Star it on Github if you like it ⭐

Bioequivalence Study Sample Size Calculator Based on R’s powerTOST package

I founded biostatistics.ca earlier this year as a project looking to provide reliable, high-quality information to the biostatistics community.

I spent a good portion of last week working on innovative tools with biostatistics.ca co-founder, and we were able to deploy an app to the website.

Humble Brag(s)

Two fascinating projects I’ve co-authored are in the process of being published in peer-reviewed papers.

In both cases, my collaborators were so enthusiastic about my work that they took the time to each share a testimonial on my website—I’m so touched!

I also gave my About Me page a makeover!

I’m curious to know what you think about it.

Revisiting Our Linkedin Live Podcast

Last month, Bobbie Rachford and I had a great time during our Live event where we discussed power calculation and sample size estimation.

A lot of people attended and a lot of great questions were asked.

We will share clips from the recording every week for the next few weeks on Linkedin!

Other (FREE) Resources You Might Like

Causal Inference Linkedin Group

Join 1.4k+ Causal Inference Enthusiasts

www.biostatistics.ca/the-biostatistics-roadmap-or-how-to-become-a-great-biostatistician

The Complete Biostatistics Roadmap, or How to Become a Great Biostatistician (with Books and Resources) - biostatistics.ca

I’m often asked by budding biostatisticians curious and eager to learn more to advise them on a developmental roadmap. Here is what I think you need to become a great applied biostatistician, which in itself is more about the journey than the destination…

www.biostatistics.ca/the-biostatistics-roadmap-or-how-to-become-a-great-biostatistician

Dear {{first_name|Causal Inference in Biostatistics Enthusiast}},

It’s Power Calculation and Sample Size Estimation Month! 🤓

Here’s the plan:

1. Free event announcement

2. Loads of resources to help you in your research!

3. Announcement for my new Causal Inference book

4. Other Resources

Linkedin Live Podcast

• What? Power Calculation and Sample Size Determination, from Basic to Complex Cases

• When? Wednesday, November 20, 12-1PM EDT

• Who? Justin Belair of JB Statistical Consulting and Robert Rachford of Better Biostats

• Q&A session at the end - my favorite part!

RSVP - To Get a Reminder and Not Miss A Thing!

Prepare for the Live Session - Recommended Reading : Why should you care?

Power Calculation : 8 Reasons Why You Should Care About It - biostatistics.ca

What is a power calculation good for? Power calculation helps a researcher determine the required sample size for a study. It is done at the planning stage of a trial. Find out more!

www.biostatistics.ca/power-calculation-8-reasons-why-you-should-care-about-it

Free Downloads Alert 🚨

5 Misconceptions Around Statistical Power and Sample Size Estimation (Nobody talks about the last one) + R Markdown Coding Notebook to Try It Yourself

The last misconception is covered in a free practice exercise on Github :

📕Causal Inference in Statistics: with Exercises, Practice Projects, and R Code Notebooks

My new book is now available for pre-order!

Each chapter contains:

• loads of exercices

• free code notebooks with case studies for applied practice

• a suggested advanced project.

Here's my plan for the release, I have around 160 pages written and I'm wrapping up Chapter 3:

1. (ASAP) Release intro chapter for free (I'm really proud of it!)

2. (~ early 2025) Release Part I - Fundamentals of Causal Inference - 4 Chapters - ~200pages :

   • Chapter 1 - Intro

   • Chapter 2 - Fundamentals of Potential Outcomes

   • Chapter 3 - Graphical Causal Models

   • Chapter 4 - Observational Studies - Design and Analysis

3. (~ mid 2025) Release Part II - 5 Chapters - The Causal Inference Toolkit - ~200pages :

   • Chapter 5 - Removing Confounding and Propensity Score Methods

   • Chapter 6 - Instrumental Variable Methods

   • Chapter 7 - Structural Equation Models and Path Analysis

   • Chapter 8 - Methods for Mediation and Interaction

   • Chapter 9 - Quasi-Experimental Designs

4. (~ end 2025) Release Part III - 3 Chapters - Advanced Causal Inference - ~200pages :

   • Chapter 10 - Longitudinal Causal Inference

   • Chapter 11 - Machine Learning and Causal AI

   • Chapter 12 - Conclusion and Next Steps

I plan on working like this:

You buy it once, and you get everything free that comes out with the book, forever.

I think it's the start of something big for me :)

PS. If you'd like to read it (for free) against feedback, DM me and we can see what we can do!

Ready to purchase it?

Need help with stats? Let’s Talk!

I’m Justin Belair – an international expert on data science and statistics. My statistical consulting and freelance work have helped researchers from all over the world understand their data.

Did you know I offer comprehensive data science and analytics services? From strategic data solutions to advanced predictive modeling, I can support your team in unlocking the potential of your data.

Ready to discuss how I can help?

Other (FREE) Resources You Might Like

Causal Inference Book

Causal Inference In Statistics : with Exercises, Practice Projects, and R Code Notebooks, By Justin Belair

justinbelair.ca/causal-inference-in-statistics-book

Free Biostatistics and Causal Inference Reading List (filled with resources)

Biostatistics resources : reading list, RMarkdown notebooks, and more! - JB-Statistical-Consulting/biostatistics

github.com/JB-Statistical-Consulting/biostatistics/blob/main/Reading%20List.md

Causal Inference Group | LinkedIn

Aleksander Molak and I administrate a group of >1k+ causal inference enthusiasts on LinkedIn. Join us! It’s an amazing community

https://www.linkedin.com/groups/13065272/