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

... and how identifiability conditions may be read off a Single World Intervention Graph (SWIG) template for the implicit DTR.

We discuss formalize connections between g-methods that use discrete-time versus continuous-time models adjustment models and the relative pros/cons of each...

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

ChatGPT explains double robustness to gen z crowd.

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?

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?

Today’s mcmc chains are invoking feelings of dread, woe, and malice.

(credit to chatgpt) #statsky

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