Bayesian Models of Treatment Effects: Model Parameterization, Prior Choice, and Posterior Summarization

Tuesday, March 22 at 4:00 PM

Causal Inference Seminar

A Causal Inference Seminar with Jared Murray (UT Austin).

Bayesian models are a popular and effective tool for inferring the (possibly heterogeneous) effects of interventions. I will discuss how to carefully specify models and prior distributions to apply judicious regularization of heterogeneous effects. I will also discuss how to extract answers to scientific and policy questions from a fitted nonparametric model using posterior summarization to avoid problems incurred by using competing or incompatible model specifications for targeting different estimands. Together these tools provide a general recipe for obtaining reliable and actionable insights from potentially complex models.

Murray is an assistant professor of statistics in the Department of Information, Risk, and Operations Management at the McCombs School of Business.