Statistician | Assistant professor at @BrownBiostats | Nonparametric Bayesian methods for causal inference. bsky.app/profile/stablemarke…
Joined March 2011
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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/…
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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
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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=kaR2…
Excerpts below start @ 19:13
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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...
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...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.
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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
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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!
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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.00000000…
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Arman Oganisian retweeted
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
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Check out this paper on Bayesian nonparametric causal inference with high-dimensional exposure mappings!
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
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24 Sep 2025
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
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24 Sep 2025
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}
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24 Sep 2025
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!
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18 Sep 2025
Congrats to @amalsargsyan & @GevTamamyan! When I first got involved, my mind went straight to the stats - but from black markets to the complexities of an out-of-pocket system, this was so much more.
One step towards better cancer policy in Armenia🤞
ascopubs.org/doi/10.1200/GO-…
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2 Sep 2025
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
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2 Sep 2025
... and how identifiability conditions may be read off a Single World Intervention Graph (SWIG) template for the implicit DTR.
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2 Sep 2025
We also discuss differences and similarities with methods for irregular visit processes that inverse-weight by the visit process intensity.
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