Bayesian Regression Tree Models for Causal Inference: Regularization, Confounding and Heterogeneity
P. Richard Hahn, School of Mathematical and Statistical Sciences, Arizona State University
In this talk I will describe a semi-parametric Bayesian regression model for estimating heterogeneous treatment effects from observational data. Standard nonlinear regression models, which may work quite well for prediction, can yield badly biased estimates of treatment effects when fit to data with strong confounding. Our Bayesian causal forests model avoids this problem by directly incorporating an estimate of the propensity function in the specification of the response model, implicitly inducing a covariate-dependent prior on the regression function. This new parametrization also allows treatment heterogeneity to be regularized separately from the prognostic effect of control variables, making it possible to informatively “shrink to homogeneity”, in contrast to existing Bayesian non- and semi-parametric approaches.
Note: Seminars are free and open to the public. Reception to follow.