I'm looking forward to teaching a 4-hour short course on Bayesian sensitivity analysis methods at the American Causal Inference Conference (ACIC) 2026! Register here: sci-info.org/annual-meeti...

Why I find Bayesian nonparametric causal inference compelling in one figure. The key distinction is btwn (1) "known" vs (2) "unknown" quantities: Make inferences about (2) conditional on (1). Want cond. avg trt effects? Condition on data, make inferences about regression lines

Arman Oganisian: Untangling Sample and Population Level Estimands in Bayesian Causal Inference https://arxiv.org/abs/2508.15016 https://arxiv.org/pdf/2508.15016 https://arxiv.org/html/2508.15016

In causal inference problems w/ sequential treatments, long stretches of time may elapse between treatment decisions This paper, in press at Epidemiology, was really fun to write: it discusses biases that may arise & corresponding adjustment via g-methods arxiv.org/abs/2508.21804

I originally wrote to share with trainees but was encouraged to post it online. I address a lot of subtleties: Why does sample-level inference need stronger assumptions? When should/n’t we impute counterfactuals? How does this differ from g-computation? Do we really need to Bayesian bootstrap?

Another distinction between imputation of counterfactuals versus monte carlo simulations used to approximate expectations in the g-formula: In the latter, you want the variance across sims (ie approx. error) to be ≈0. In the former, variance imputation should propagate to reflect uncertainty.

New paper on Bayesian Diff-in-Diff methods: www.arxiv.org/abs/2508.02970 When doing DiD, many inspect the difference in trends in the pre-period to “check” whether parallel trends (PT) holds. But PT is fundamentally uncheckable since it must hold in the post-period as well. What’s going on?

Thanks for linking! I also have a set of slides with Stan code from a recent half-day short course: Slide deck 1 is just a primer on Bayesian inference. Slide deck 2 is on the Bayesian causal stuff. github.com/stablemarket...

I’ve seen so many instances of conflating sample and population estimands when doing Bayesian causal inference in conference talks, papers on arxiv, papers i’ve reviewed, and even published papers. People often claim to be doing one when actually doing the other.

I’m teaching a 3-hour session on Bayesian causal inference at this year’s Penn Causal Inference Summer Institute, 5/27-5/30. Virtual registration/attendance options are available. There are sessions on a lot of other great topics - see full agenda here: dbei.med.upenn.edu/news-events/... #statsky

Reminder to self to post my lecture notes on first-order equivalence between bayesian bootstrap SEs, frequentist bootstrap SEs, and sandwich SEs for a linear model with heteroskedastic errors

Academia is cool because if you're doing it right, every paper you published in the last 3 years feels inadequate now that you understand the topic better, but it'll take 3 years to get out the version where you get it more right, and you get to do that until one day you die! Isn't that cool

Congratulations to our very own Arman Oganisian, Assistant Professor of Biostatistics, for receiving the 2025 SPH Dean’s Award for Excellence in Research Collaboration! 🏆 We’re so proud to celebrate your achievement!

New paper w/ Tony Linero on Bayesian causal inference: Independent priors on propensity score & outcome models often imply a strong prior on no *measured* confounding - a prior belief that 1) we rarely hold and 2) leads to bad frequentist performance tinyurl.com/2udmbf6a #statsky

Today’s mcmc chains are invoking feelings of dread, woe, and malice. (credit to chatgpt) #statsky

I’ll be at #ENAR2025 to talk about a recent paper on Bayesian causal inference with a recurrent event outcomes! Session 50: Monday 1:45-3:30 Talk info: www.enar.org/meetings/spr... Full paper: academic.oup.com/biometrics/a... #StatsSky

Bayesian Causal Inference w/ survival outcomes has never been so easy! Check out work by Biostats PhD student Han Ji now accepted at Observational Studies. Convenient syntax, help files, custom S3 classes, & efficient MCMC via Stan in back-end arxiv.org/pdf/2310.12358 github.com/RuBBiT-hj/ca...

So many of the responses go down a Bayesian road - it’s inevitable if all models are indeed equally plausible. Reminds me of one of my favorite quotes from Radford Neal

Software update: Daniel Kowal (Cornell) was kind enough to include an implementation of our hierarchical Bayesian bootstrap in his SeBR R package (which also has other great regression tools!) - complete w/ help files and examples. t.co/ekVZXcDqJy t.co/LuTO9VSJfs

Finished drafting lecture notes on two of my favorite results in Bayesian inference: 1) The empirical Bayes derivation of the James-Stein Estimator 2) the (first order) equivalence of Bayesian bootstrap covariance, Efron’s bootstrap covariance, and the robust sandwich covariance estimators

Check out this new paper by Biostatistics PhD Candidate Esteban Fernández-Morales. He develops innovative Bayesian spatial shrinkage methods for causal inference with spillovers and uses it to assess the effect of Philadelphia's 2017 beverage tax. arxiv.org/abs/2501.08231