A1507
Title: Robust semiparametric inference for Bayesian additive regression trees
Authors: Ruixuan Liu - Chinese University of Hong Kong (Hong Kong) [presenting]
Abstract: A semiparametric framework is developed for inference on the mean response in missing-data settings, using a corrected posterior distribution. Our approach is tailored to Bayesian Additive Regression Trees (BART), a powerful predictive method, but its nonsmoothness complicates asymptotic theory for multidimensional covariates. When using BART combined with Bayesian bootstrap weights, we establish a new Bernstein von Mises theorem and show that the limit distribution generally contains a bias term. To address this, we introduce RoBART, a posterior bias-correction that robustifies BART for valid inference on the mean response. Monte Carlo studies support our theory, demonstrating reduced bias and improved coverage relative to existing procedures using BART.
