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Activity Number: 14 - Data Science with Semiparametric Bayes
Type: Invited
Date/Time: Sunday, July 30, 2017 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #321900 View Presentation
Title: Bayesian Causal Forests
Author(s): Richard Hahn* and Jared S Murray and Carlos Carvalho
Companies: University of Chicago and Carnegie Mellon University and University of Texas at Austin
Keywords: Causal inference ; regression trees ; regularization ; heterogeneous treatment effects

In this talk I will describe a semi-parametric Bayesian regression model for estimating heterogenous 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 fitted to data with strong confounding. The new Bayesian causal forest model is able to eliminate this adverse bias by jointly modeling the treatment and the response conditional on control variables. Two empirical illustrations are given, analyzing the impact of smoking on medical expenditures and the impact of abortion laws on future crime rates.

Authors who are presenting talks have a * after their name.

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