As the great American philosopher Russell Stringer Bell said, I want you to put the word out there that we back up. Stop by the SBSS+ ISBA table in the exhibition hall at #JSM2026 and find out about all the exciting Bayesian sessions taking place (and get some candy) @isba-bayesian.bsky.social

Cross posting my response to a thread (www.linkedin.com/posts/richar...) about the “hazard of the hazards” on LinkedIn - the original thread was unfairly dismissive of claims that contrasts of hazards are non-causal. I think it’s important to understand such claims fully - so here it goes.

It is frequently claimed that hazard ratios are somehow “non-causal” or have “inherent selection bias”. I think the associated critique is really about collapsibility, rather than causality or bias… |...

It is frequently claimed that hazard ratios are somehow “non-causal” or have “inherent selection bias”. I think the associated critique is really about collapsibility, rather than causality or bias. (...

linkedin.com

Happy to see this paper out. It deals with a practical question in Bayesian causal inference: “𝘈𝘳𝘦 𝘺𝘰𝘶 𝘳𝘦𝘢𝘭𝘭𝘺 𝘤𝘰𝘮𝘱𝘶𝘵𝘪𝘯𝘨 𝘦𝘴𝘵𝘪𝘮𝘢𝘵𝘦𝘴 𝘰𝘧 𝘵𝘩𝘦 𝘤𝘢𝘶𝘴𝘢𝘭 𝘦𝘴𝘵𝘪𝘮𝘢𝘯𝘥 𝘺𝘰𝘶 𝘵𝘩𝘪𝘯𝘬 𝘺𝘰𝘶 𝘢𝘳𝘦 𝘢𝘯𝘥 𝘶𝘯𝘥𝘦𝘳 𝘵𝘩𝘦 𝘢𝘴𝘴𝘶𝘮𝘱𝘵𝘪𝘰𝘯𝘴 𝘺𝘰𝘶 𝘵𝘩𝘪𝘯𝘬 𝘺𝘰𝘶'𝘳𝘦 𝘮𝘢𝘬𝘪𝘯𝘨?” Short answer: you may not be. www.degruyterbrill.com/document/doi...

New paper in press at Biometrics by PhD Candidate Esteban Fernández-Morales 1) Develops Bayesian spike & slab and horseshoe models for causal inference under spatial spillover 2) Analyzes Philly's 2017 beverage tax accounting for cross-border shopping arxiv.org/pdf/2501.08231

Teaching regression in my Bayes class and one thing I don’t like is language about whether we “treat X as fixed” or “treat X as random”. Both X and Y are random draws from a joint F_{X,Y}. It’s just that we factorize it as F_{X,Y}= F_{Y|X} F_{X} w/interest in E[Y|X] = ∫y dF_{Y|X}.

The critique of unmeasured confounding is often levied in a lazy/broad way. It is trivially true in any observational study. But if the critic can't think of a plausible such confounder and posit a reasonable direction/magnitude of its bias then they're not doing productive science.

Arman Oganisian@stablemarkets.bsky.social · 5mo ago

We really do need to get away from the binary. It should be "is there unmeasured confounding or not." Almost certainty there is - it's a matter of how much and what direction.

100% prediction interval

Chris Paxton@cpaxton.bsky.social · 5mo ago

Easily one of the top charts of all time

This paper is now out in final form and is open-access! journals.lww.com/epidem/fullt...

Arman Oganisian@stablemarkets.bsky.social · 11mo ago

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'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?

Adam L@adam-lg.bsky.social · 12mo ago

A cool-looking paper from @stablemarkets.bsky.social: "Untangling Sample and Population Level Estimands in Bayesian Causal Inference" Paper: arxiv.org/abs/2508.15016 Code: github.com/stablemarket... #statssky #mlsky

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...

GitHub - stablemarkets/cci_institute_2025: Materials for Bayesian Causal Inference Sessions @ University of Pennsylvania's Center for Causal Inference (CCI)'s summer institute. May 29, 2025

Materials for Bayesian Causal Inference Sessions @ University of Pennsylvania's Center for Causal Inference (CCI)'s summer institute. May 29, 2025 - stablemarkets/cci_institute_2025

github.com

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

2025 Penn Causal Inference Summer Institute - Penn DBEI

Discover the latest news, research breakthroughs, and expert insights from Penn’s DBEI, advancing biostatistics, epidemiology, and informatics to shape population health.

dbei.med.upenn.edu

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

Dr Mircea Zloteanu 🌺🌞🍃@mzloteanu.bsky.social · last yr.

#statstab #293 The Bayesian Bootstrap Thoughts: I need to think more on where bootstrapping makes sense in a bayesian setting. But here's a tutorial. #stats #bayesian #bayes #bootstrap #resampling towardsdatascience.com/the-bayesian...

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

A Bayesian framework for causal analysis of recurrent events with timing misalignment

Abstract. Observational studies of recurrent event rates are common in biomedical statistics. Broadly, the goal is to estimate differences in event rates u

academic.oup.com

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

John Holbein@johnholbein1.bsky.social · last yr.

Methods question: I'm estimating an effect of a binary IV on a continuous DV. Let's say there are 6 equally plausible model specifications that I can run. I run the 6 specifications and 4 out of the 6 specifications have p<0.05. The other two are not significant, but are in the same direction...

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