Causal Inference Seminar with Ken McAlinn.
We propose a new Bayesian methodology to mitigate misspecification in estimating treatment effects. A plethora of methods to estimate– particularly the heterogeneous– treatment effect have been proposed with varying success. It is recognized, however, that the underlying data generating mechanism can drastically affect the performance of each method, without any way to compare its performance in real world applications. Using a foundational Bayesian framework, we develop Bayesian causal synthesis; a meta-inference method that synthesizes several causal estimates for improved inference. We provide a fast posterior computation algorithm and show that the proposed method provides consistent estimates of the heterogeneous treatment effect. Several simulations and an empirical study highlight the efficacy of the proposed approach compared to existing methodologies, providing improved point and density estimation.
Ken is an Assistant Professor of Statistical Science at Temple University, Fox School of Business. Before Temple, he was a Senior Research Professional in Econometrics and Statistics at The University of Chicago, Booth School of Business. He received his Ph.D. in statistical science at Duke University in the Department of Statistical Science and his Ph.D. in Economics at Keio University in the Department of Economics. He also has a M.S. in statistical science from Duke University and a dual masters from Keio University (Economics) and L’Institute D’Etudes Politiques De Paris (Economics and Public Policy, joint with Ecole Polytechnique and ENSAE). As an undergraduate at Keio University, he received a B.A. in Economics with a focus on Bayesian econometrics and film theory.