Bayesian

This post is a brief summary of highlights from our recent working paper titled “Bayesian Semiparametric Model for Sequential Treatment Decisions with Informative Timing.” It’s part of a series of projects on developing robust Bayesian nonparametric models for sequential decision making in a variety of complex settings.

Writing up a quick post to clarify a point of common confusion when doing posterior inference for causal effects. All causal inference (regardless of statistical modeling paradigm in consideration) begins with expressing the target estimand in terms of unobservables (e.

This post is an add-on to my previous post about augmented gibbs sampling for censored survival times. If you’re not a complete maniac like me, then you probably don’t want to code your own sampler from scratch like I did in that previous post.