Theiss Bendixen @theissbendixen.bsky.social · 16/09/2026Agree, the paper seems fairly balanced. Of course an informative priors requires justification and sensitivity analysis. But I think that principle should apply in the other direction too, such that a frequentist analysis is required to justify the relevant data that it ignores. 110
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/ 0194
Theiss Bendixen @theissbendixen.bsky.social · 09/09/2026Cool, thanks! Is there a vignette somewhere on the marginalisation approach to group-level parameters? 100
Theiss Bendixen @theissbendixen.bsky.social · 04/09/2026Big congrats -- and big thanks for a great piece of software! Can you say a little more on how to get the "unconditional" standard error out? Tried to look at a few case studies but am probably overlooking something - thanks again! 120
Reposted by Theiss BendixenTheiss Bendixen @theissbendixen.bsky.social · 05/06/2026New 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/ 194
Reposted by Theiss BendixenTheiss Bendixen @theissbendixen.bsky.social · 06/06/2026It'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 👇 38522
Reposted by Theiss BendixenAndrew Gelman et al. @statmodeling.bsky.social · 25/08/2026Bayesian Workflow free pdf! statmodeling.stat.columbia.edu/2026/08/25/b...statmodeling.stat.columbia.edu Bayesian Workflow free pdf! | Statistical Modeling, Causal Inference, and Social Science 17429
Theiss Bendixen @theissbendixen.bsky.social · 18/08/2026Hi Frank, I sent you an email re. this, at fh@fharrell.com 😊 Best wishes 010
Theiss Bendixen @theissbendixen.bsky.social · 28/07/2026Nice, thanks! Agree, robustifying a prior is often useful. There are also approaches like "Bayesian dynamic borrowing" that increase borrowing when data and prior align and reduce borrowing in case of conflict - quick intro here: theissbendixen.com/bayesian-dyn...theissbendixen.comBeing Bayesian in a Frequentist World 000
Theiss Bendixen @theissbendixen.bsky.social · 23/07/2026McElreath's Statistical Rethinking (2nd ed) has Stan blocks explained bit by bit in certain chapters? 😊 Also the model syntax in the {rethinking} package can work as a scaffold towards learning Stan. @rmcelreath.bsky.social 100
Theiss Bendixen @theissbendixen.bsky.social · 15/07/2026Some of the best statisticians I know are not ✨ real statisticians ✨ 030
Reposted by Theiss BendixenTheiss Bendixen @theissbendixen.bsky.social · 13/07/2026Right! 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.comThe Evolution of Big Brains | The Evolution of Big Brains | InferenceThe 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 ... 041
Theiss Bendixen @theissbendixen.bsky.social · 13/07/2026Right! 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.comThe Evolution of Big Brains | The Evolution of Big Brains | InferenceThe 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 ... 041
Theiss Bendixen @theissbendixen.bsky.social · 06/07/2026This 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.! 0212
Reposted by Theiss BendixenTheiss Bendixen @theissbendixen.bsky.social · 03/07/2026New 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... 23522
Theiss Bendixen @theissbendixen.bsky.social · 03/07/2026And of course, all data and code are freely available -- check out the paper for details. www.sciencedirect.com/science/arti...sciencedirect.comEcological not social factors explain brain size in cephalopodsSocial factors have been argued to be the main selection pressure for the evolution of large brains and complex behavior, but many cephalopods live la… 020
Theiss Bendixen @theissbendixen.bsky.social · 03/07/2026Oh, and by the way, we have *much* more data from this project than we present here. So feel free to reach out for potential collaboration! 🙌 @michael.muthukrishna.com 110
Theiss Bendixen @theissbendixen.bsky.social · 03/07/2026In short, cephalopods are an evolutionary enigma and a promising animal group on which to evaluate prominent hypotheses on the evolutionary drivers of brain size. So that's what we set out to do! 140
Theiss Bendixen @theissbendixen.bsky.social · 03/07/2026Cephalopods are also mostly short-lived, asocial and cannibalistic, they exhibit little to no parental care or pair-bonding, and usually die shortly after first reproduction. This runs counter to theories of brain size and intelligence in large-brained vertebrates. 120
Theiss Bendixen @theissbendixen.bsky.social · 03/07/2026Cephalopods are phylogenetically very distant from animals usually considered “brainy,” branching off from the vertebrates over 500 million years ago. And yet, many cephalopods possess large and complex nervous systems and exhibit flexible behaviours. 110
Theiss Bendixen @theissbendixen.bsky.social · 03/07/2026In short: good ol' slow-cooked, sous vide science 🤌✨ 110
Theiss Bendixen @theissbendixen.bsky.social · 03/07/2026For instance, we reviewed the ecology, behavior, life-history and physiology of all ceph species for which we have brain size data, and we also built a phylogeny of these species to allow for statistical modeling of phylogenetic relationships. Phylogeny pre-print: www.biorxiv.org/content/10.1... 110
Theiss Bendixen @theissbendixen.bsky.social · 03/07/2026First of all, this was a *huge*, multi-year (+7 years) team effort! My own research has taken a few turns since we began this, but our review, database and analyses are still a massive contribution to the field of cephalopod and comparative brain studies, in my humble opinion. 100
Theiss Bendixen @theissbendixen.bsky.social · 03/07/2026New 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... 23522
Reposted by Theiss BendixenKristoffer Magnusson @rpsychologist.com · 25/06/2026New interactive blog! "Why Adjusted Regression Coefficients Are Less Descriptive Than They Look" rpsychologist.com/descriptive-... 1520769
Theiss Bendixen @theissbendixen.bsky.social · 26/06/2026Brilliant 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 121
Theiss Bendixen @theissbendixen.bsky.social · 11/06/2026Can't get a specific date but it'll roll out over the next few weeks/months -- apparently this is normal for these kinds of books (?) 110
Theiss Bendixen @theissbendixen.bsky.social · 11/06/2026Thanks so much for spotlighting "The Data Analyst's Guide to Cause and Effect"! 📚🙌 000
Theiss Bendixen @theissbendixen.bsky.social · 09/06/2026It seems to be delayed outside US. But maybe we can arrange a physical copy at AU through one of @bgpurzycki.bsky.social author copies? Then I'll owe Ben one 😀 110
Theiss Bendixen @theissbendixen.bsky.social · 08/06/2026Huh, good question, thanks! I'm sure it'll be available at some point (I actually thought it already was), but will check 👍 010
Theiss Bendixen @theissbendixen.bsky.social · 07/06/2026Related to this 👇 bsky.app/profile/thei... 100
Theiss Bendixen @theissbendixen.bsky.social · 07/06/2026There's also a bonus section on "non-centered" parameterisation 🤓 100
Theiss Bendixen @theissbendixen.bsky.social · 07/06/2026For fitting the MBNMA, I use @rmcelreath.bsky.social's rethinking package, where the syntax satisfyingly mirrors the formal model. 110
Theiss Bendixen @theissbendixen.bsky.social · 07/06/2026I give a brief introduction to Bayesian model-based network meta-analysis (MBNMA) to model studies on different dose levels of the same drug and show how it can be used to inform an indirect treatment comparison between competitive drugs that have not been studied in the same trial. 100
Theiss Bendixen @theissbendixen.bsky.social · 07/06/2026It’s common for meta-analyses in the clinical trial literature to lump together studies with different doses, follow-up times, or populations. But often we can do better than that and instead model the dependencies or discrepancies explicitly. One potential payoff is increased statistical precision. 100
Reposted by Theiss BendixenTheiss Bendixen @theissbendixen.bsky.social · 06/06/2026Instead, 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.comThe Data Analyst's Guide to Cause and EffectThis is the companion website for The Data Analyst's Guide to Cause and Effect 121
Reposted by Theiss BendixenTheiss Bendixen @theissbendixen.bsky.social · 06/06/2026It 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. 111
Reposted by Theiss BendixenTheiss 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 111
Reposted by Theiss BendixenTheiss 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 121
Reposted by Theiss BendixenTheiss Bendixen @theissbendixen.bsky.social · 06/06/2026First, 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 121
Theiss Bendixen @theissbendixen.bsky.social · 06/06/2026Published with @sagepub.com More here: collegepublishing.sagepub.com/products/the...collegepublishing.sagepub.comThe Data Analyst’s Guide to Cause and EffectUnderstanding cause-and-effect relationships is essential for credible research and informed decision-making. The Data Analyst’s Guide to Cause and Effect offers a clear, practical ... 010
Theiss Bendixen @theissbendixen.bsky.social · 06/06/2026Instead, 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.comThe Data Analyst's Guide to Cause and EffectThis is the companion website for The Data Analyst's Guide to Cause and Effect 121
Theiss Bendixen @theissbendixen.bsky.social · 06/06/2026It 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. 111
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 111
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 121
Theiss Bendixen @theissbendixen.bsky.social · 06/06/2026First, 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 121
Theiss Bendixen @theissbendixen.bsky.social · 06/06/2026It'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 👇 38522
Theiss Bendixen @theissbendixen.bsky.social · 05/06/2026New 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/ 194
Theiss Bendixen @theissbendixen.bsky.social · 03/06/2026Totally agree! We aspire to do this in The Data Analyst's Guide to Cause and Effect, obv. inspired by folks like @rmcelreath.bsky.social and @statmodeling.bsky.social theissbendixen.com/dag-book/theissbendixen.comThe Data Analyst's Guide to Cause and EffectThis is the companion website for The Data Analyst's Guide to Cause and Effect 010