Arman Oganisian

I'm looking forward to teaching a 4-hour short course on Bayesian sensitivity analysis methods at the American Causal Inference Conference (ACIC) 2026! It will have a distinct focus on practical implementation in @mcmc_stan w/ examples. Register here: sci-info.org/annual-meeting…

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

...you have a big monster hanging out in front of you that can make you cheat on everything ...contrast, aperture, lighting. It's your duty to decide how much you're going to let yourself fall down that well.

Jack: I'm a believer that new technology needs new responsibility. If you're a photographer you have photoshop now. And if you're going to call yourself a photographer and dedicate your life to it...

With rapid AI advancement, I've been thinking more about this conversation 13 yrs ago, btwn Jack White & Conan O'Brien, and how it's as relevant for statistics/data science education now as it is for music, comedy, film, etc. youtu.be/AJgY9FtDLbs?si… Excerpts below start @ 19:13

@biostatsfun @mcmc_stan I think it’s worth the investment! Section 2.1 provides a minimally sufficient overview of causal inference in stan for the purposes of the paper.

@StableMarkets @mcmc_stan I see, I’ll need to learn Stan then 😄. Thank you for your reply.

New paper up on arXiv. “Stress-Testing Assumptions: A Guide to Bayesian Sensitivity Analyses in Causal Inference” Examples include: exposure misclassification, unmeasured confounding, snd MNAR outcomes. W/ @mcmc_stan code on GitHub. arxiv.org/abs/2602.23640

@biostatsfun @mcmc_stan Stan wrappers like causalBETA and brms (perhaps) are too high-level. E.g. the unmeasured confounding example requires a joint outcome and treatment model with missing confounder value integrated out. So can’t easily accommodate this.

@StableMarkets @mcmc_stan Dr. Oganisian, this paper is great. I’m more used to coding with brms or causalBETA R packages. Would it be too much trouble to show implementation with brms /causalBETA syntax? Would be especially interested in the unmeasured confounding example. Thank you.

@jesper_wulff @mcmc_stan arxiv.org/abs/2602.23640

@jesper_wulff @mcmc_stan Probably not near-term since I hope to offer it at future conferences. I have a companion paper under review at Statistics in Medicine’s “Tutorials in Biostatistics” series. That should hopefully be out sooner w/ a companion github repo. I’ll post when it is!

I'm looking forward to teaching a 4-hour short course on Bayesian sensitivity analysis methods at the American Causal Inference Conference (ACIC) 2026! It will have a distinct focus on practical implementation in @mcmc_stan w/ examples. Register here: sci-info.org/annual-meeting…

Just got this ad while scrolling BBC 😂 I suppose this is partially a result of using ML models w.o. good causal/statistical reasoning. Anyway I guess apply to the online @BrownBiostats ScM program to learn how to not do this!

New paper on causal inference with irregularly (and informatively) timed treatment decisions. The paper has publicly available code and via generosity of @PCORI is open-access: doi.org/10.1097/EDE.00…

Our new paper is out: Bayesian nonparametric causal inference for high-dimensional nutritional data using factor-based exposure mapping. arxiv.org/abs/2601.16595 Joint work with my postdoc Dafne Zorzetto and collaborators @StableMarkets @BrownBiostats

Check out this paper on Bayesian nonparametric causal inference with high-dimensional exposure mappings!

@Bakaburg1 Hi there’s no recording but i have slides here: github.com/stablemarkets/…

@StableMarkets Hi! Is there a recording of the lecture somewhere?

I’m teaching a 3-hour session on Bayesian causal inference at Penn Causal Inference Summer Institute, 5/27-5/30. Virtual attendance options are available. There are sessions on many other really cool topics as well -check out the agenda: dbei.med.upenn.edu/news-events/20…

@matloff That's a pretty strong prior you have there!

@StableMarkets Yes, the Bayesian approach is very neat mathematically. But it flies in the face of the scientific method, and should not be used in science research.

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

Bayesian nonparametric models allow flexibility in regions w/ lots of data, while allowing priors about sensitivity parameters drive inference in regions w/o data (see bottom-right plot). Uncertainty about *all* unknowns flow into a single posterior for the causal quantity!

Are some patients missing outcome info? Condition on data, make inferences about unknown {regression lines & missing values}. Think the missingness is not at-random? Condition on data, making inferences about unknown {regression lines, missing values, & sensitivity parameters}