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Theiss Bendixen @theissbendixen.bsky.social · 10/09/2026

"Descriptive statistics also require causal inference" So true! In the Data Analyst's Guide, we illustrate it with a fun example: a registry study on spirituality among Danes 👇 theissbendixen.com/dag-book/

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Theiss Bendixen @theissbendixen.bsky.social · 05/06/2026

New blog post! 🚨 "From Bucher to Bayes: A Brief Introduction to Bayesian Model-Based Network Meta-Analysis for Indirect Treatment Comparisons using R" theissbendixen.com/mbnma/

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Theiss Bendixen @theissbendixen.bsky.social · 06/06/2026

It's alive! 🎉 𝗧𝗵𝗲 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁'𝘀 𝗚𝘂𝗶𝗱𝗲 𝘁𝗼 𝗖𝗮𝘂𝘀𝗲 𝗮𝗻𝗱 𝗘𝗳𝗳𝗲𝗰𝘁 is out -- an introduction to causal inference in practice. The first two chapters are available for free here: theissbendixen.com/dag-book/ More below 👇

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Theiss Bendixen @theissbendixen.bsky.social · 13/07/2026

Right! Our efforts were in fact guided by a model of brain size [1], which was originally motivated by primates, but also makes predictions for an asocial path to big brains that seems to characterise the cephs [2] [1] journals.plos.org/ploscompbiol... [2] inference-review.com/letter/the-e...

inference-review.com

The Evolution of Big Brains | The Evolution of Big Brains | Inference

The cultural brain hypothesis predicts two main paths to intelligence and large brains in animals: a social learning path taken by humans at one end, and an asocial learning path taken by cephalopods ...

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Theiss Bendixen @theissbendixen.bsky.social · 06/07/2026

This arrived just in time for Danish summer weather (rain). A few chapters in and it does not dissappoint! Remarkable mix of theory and practice, so many good points to absorb -- hope it's widely read. Thanks @statmodeling.bsky.social, @avehtari.bsky.social, @rmcelreath.bsky.social, et al.!

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Theiss Bendixen @theissbendixen.bsky.social · 03/07/2026

New research! 📚 We built the largest database to date of cephalopod species - octopuses, squids and cuttlefish - and their brains, habitats and behaviors 🐙🧠📈 What did we find?👇 Press release: www.lse.ac.uk/news/ecologi... Paper: www.sciencedirect.com/science/arti...

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Theiss Bendixen @theissbendixen.bsky.social · 03/07/2026

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Kristoffer Magnusson @rpsychologist.com · 25/06/2026

New interactive blog! "Why Adjusted Regression Coefficients Are Less Descriptive Than They Look" rpsychologist.com/descriptive-...

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Theiss Bendixen @theissbendixen.bsky.social · 26/06/2026

Brilliant read! I'd add that several of the big COVID trials took an explicit Bayesian approach, which facilitates interim looks at the data and allows a trial to stop if the treatment is clearly working (or not). @statberry.bsky.social gives a readable overview here: www.mdpi.com/2077-0383/14...

mdpi.com

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Theiss Bendixen @theissbendixen.bsky.social · 11/06/2026

Thanks so much for spotlighting "The Data Analyst's Guide to Cause and Effect"! 📚🙌

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Theiss Bendixen @theissbendixen.bsky.social · 06/06/2026

Instead, we cut to the chase and emphasize a practical workflow using step-by-step explanations and real data examples in R. The companion website lives here theissbendixen.com/dag-book and holds: - All data and code used in the book - Free sample chapters - Bonus material!

theissbendixen.com

The Data Analyst's Guide to Cause and Effect

This is the companion website for The Data Analyst's Guide to Cause and Effect

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Theiss Bendixen @theissbendixen.bsky.social · 06/06/2026

It took us three years to write this thing. But the good news is you can read it in three days! We cover fairly advanced methods -- counterfactuals, g-computation, inverse probability of treatment weighting, poststratification, missing data imputation, etc. -- without dense formal notation.

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Theiss Bendixen @theissbendixen.bsky.social · 06/06/2026

"Strongly application-focused... an effective tool for getting data analysts into the world of causal inference and immediately into a workable project." -- Nick Huntington-Klein, @nickchk.com

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Theiss Bendixen @theissbendixen.bsky.social · 06/06/2026

"An excellent, comprehensive, yet accessible introduction to causal inference... an invaluable guide for analysts seeking to move beyond mere correlation." -- Julia Rohrer, @dingdingpeng.the100.ci

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Theiss Bendixen @theissbendixen.bsky.social · 06/06/2026

First, we're very lucky that some very impressive people have already said some very nice words about the book! "A clear and readable book with broad coverage of many ideas and methods in causal inference." -- Andrew Gelman, @statmodeling.bsky.social

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Theiss Bendixen @theissbendixen.bsky.social · 06/06/2026

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Theiss Bendixen @theissbendixen.bsky.social · 05/06/2026

New blog post! 🚨 "From Bucher to Bayes: A Brief Introduction to Bayesian Model-Based Network Meta-Analysis for Indirect Treatment Comparisons using R" theissbendixen.com/mbnma/

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Theiss Bendixen @theissbendixen.bsky.social · 26/05/2026

Nice! Similar phenomenon to assurance (or marginal/average power), where a prior is placed over the effect and power is integrated over it to account for uncertainty, rather than conditioning on a single point estimate. Assurance is also always lower than power in practice.

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Theiss Bendixen @theissbendixen.bsky.social · 07/05/2026

Here's my current tentative and very much in progress outline. Comments of all kind much appreciated!

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Theiss Bendixen @theissbendixen.bsky.social · 07/05/2026

Imagine a short book (~200 p.) introducing Bayesian statistics in the context of clinical trials and drug development. Scope would be introductory -- sort of "your first short course on Bayes". But practical enough to be applied out of the box. What would you like to see covered in such a text? 👇

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Theiss Bendixen @theissbendixen.bsky.social · 22/05/2026

Yes, very cool! It's a class of methods often referred to as "Bayesian dynamic borrowing," and it's not well-known outside the clinical trial literature (and even there it's not very common). I give a brief intro to one particular approach here: theissbendixen.com/bayesian-dyn...

theissbendixen.com

Being Bayesian in a Frequentist World

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Ryan Briggs @ryancbriggs.net · 22/05/2026

In the Fall I'll be teaching a new MA-level methods course entitled "Applied Statistical Evaluation of Development Projects". It will be 12 weeks, in R, and aimed around RCT evaluations. This is a draft outline. What am I missing? What seems redundant?

1. Course Introduction and Setup  Course overview; installing RStudio; introduction to causal inference; ModernDive Chapters 1–2 for newcomers.
2. Data, Tidy Data, Wrangling, and Visualization  Core R skills for importing, cleaning, reshaping, summarizing, and visualizing evaluation data.
3. Sampling, Uncertainty, and Inference  Sampling variation, confidence intervals, hypothesis testing, and the logic of statistical uncertainty.
4. Difference in Means as Regression  Equivalence between difference-in-means estimates and lm(y ~ treat); ATE as the treatment coefficient; control mean as the intercept; covariates for precision gains; simulations and re-analysis of Karlan–List charity data.
5. Interactions and Treatment Effect Heterogeneity  Interaction terms, subgroup analysis, heterogeneous effects; simulations, Karlan–List charity data, and Thornton HIV data.
6. Standard Errors, Power, and Research Design  Bias, variance, RMSE, clustering, power analysis, and how underpowered studies contribute to selection on significance and inflated estimates.
7. Noncompliance, Take-Up, and Instrumental Variables  ITT, TOT, LATE, compliers etc, and randomized encouragement designs; Thornton HIV testing incentives; reading from The Effect Chapter 19 or Causal Inference: The Mixtape IV chapter.
8. Spillovers, Externalities, and Peer Effects  How spillovers can bias experimental estimates; identifying, measuring, and interpreting spillover effects in development evaluations.
9. Pre-Analysis Plans, Measurement, and Cost-Effectiveness  PAPs, outcome measurement, measurement error, index construction, and basic cost-effectiveness analysis.
10. Meta-Analysis and Evidence Aggregation  Fixed-effect and random-effects meta-analysis; Bayesian meta-analysis using baggr; interpreting accumulated evidence across studies.
11. Case Study: Deworming Evidence I  Critical re-analysis of the main deworming results; statistical interpretation; cost-effectiveness implications.

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Robert (Bob) Kubinec @rmkubinec.bsky.social · 21/05/2026

🚨 Blog post: When Using OLS Hurts 😩 I replicated a high-profile study on racial bias in tenure decisions and show the authors weakened their own findings by using OLS instead of ordered beta regression 🤯. Use ordered beta and live your best life 👍 #rstats www.robertkubinec.com/post/ord_bet...

robertkubinec.com

When Using OLS Hurts – Homepage

People often use OLS for bounded continuous variables even though we know it isn’t the correct model. Ordered beta regression is a better model–but hard to predict when the results will change. For th...

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Theiss Bendixen @theissbendixen.bsky.social · 07/05/2026

This gives a flavor of the style: theissbendixen.com/bayesian-dyn... The scope (short, introductory, applied) is also similar to our forthcoming causal inference book: us2.sagepub.com/en-us/nam/th...

theissbendixen.com

Being Bayesian in a Frequentist World

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Theiss Bendixen @theissbendixen.bsky.social · 07/05/2026

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Theiss Bendixen @theissbendixen.bsky.social · 06/05/2026

Yeah, Bayes is usually thought of as particularly useful with sparse data (because priors can do some of the work), but I think it's equally true that Bayes is useful when there's a lot of good data on e.g. a drug, because Bayes is very well-suited to exploit all that information in a principled way

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Theiss Bendixen @theissbendixen.bsky.social · 04/05/2026

Bayes is sometimes used at various stages in drug development. For instance in fancy meta-analysis: dmphillippo.github.io/multinma/ Borrowing in clinical trial analysis (worked example and some literature): theissbendixen.com/bayesian-dyn... Adaptive trials: hbiostat.org/doc/bayes/wh...

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