Sofia Triantafyllou: A Bayesian Method for Causal Inference with Observational and Experimental Data

Sofia Triantafyllou (University of Crete) Title: A Bayesian Method for Causal Effect Estimation with Observational and Experimental Data Discussant: Shu Yang (NC State University) Abstract: Decision making is often about selecting the intervention that will maximize an outcome. In healthcare settings, for example, for each patient the goal is to select the treatment that will optimize the patient’s clinical outcome. Experimental data from randomized controlled trials allow for unbiased estimation of post-intervention outcome probabilities but are usually limited in the number of samples and the set of measured covariates. Observational data, such as electronic medical records, contain many more samples and a richer set of measured covariates, which we can use to estimate more personalized treatment effects; however, these estimates may be biased due to latent confounding. In this talk, we describe a Bayesian method that uses causal graphical models to combine observational and experimental data to improve causal effect estimation, when possible. In particular, we discuss how to select optimal feature sets in order to combine experimental and observational data to predict a post-intervention outcome, when observational data allow for unbiased estimation of that outcome.