RMME/STAT Joint Colloquium:
The Bayesian Causal Forest Model: Regularization, Confounding, and Heterogeneous Effects
Arizona State University
January 29, 2021, at 12:00 EST
This talk will describe recent work on Bayesian supervised learning for conditional average treatment effects. Dr. Hahn will motivate the proposed Bayesian causal forest model in terms of fixing two specific flaws with previous approaches. One, our model allows for direct regularization of the treatment effect function, providing lower variance estimates of heterogeneous treatment effects. Two, by including an estimate of the propensity score as a control variable in our model we mitigate a phenomenon called “regularization induced confounding” that leads to substantial bias in previous approaches. Dr. Hahn will conclude with a detailed discussion of designing simulation studies to systematically investigate and validate machine learning models for causal inference.
Note: Dr. Hahn may also talk about this tutorial: https://math.la.asu.edu/~prhahn/xbcf_demo.html