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计量、金融与大数据分析workshop：Double Robust Bayesian Inference on Average Treatment Effects
发布日期：2023-11-24 12:00 来源：
题目：Double Robust Bayesian Inference on Average Treatment Effects
摘要：We study a double robust Bayesian inference procedure on the average treatment effect (ATE) under unconfoundedness. Our robust Bayesian approach involves two adjustment steps: first, we make a correction for prior distributions of the conditional mean function; second, we introduce a recentering term on the posterior distribution of the resulting ATE. We prove asymptotic equivalence of our Bayesian estimator and double robust frequentist estimators by establishing a new semiparametric Bernstein--von Mises theorem under double robustness; i.e., the lack of smoothness of conditional mean functions can be compensated by high regularity of the propensity score and vice versa. Consequently, the resulting Bayesian point estimator internalizes the bias correction as the frequentist-type doubly robust estimator, and the Bayesian credible sets form confidence intervals with asymptotically exact coverage probability. In simulations, we find that this robust Bayesian procedure leads to significant bias reduction of point estimation and accurate coverage of confidence intervals, especially when the dimensionality of covariates is large relative to the sample size and the underlying functions become complex. We illustrate our method in an application to the National Supported Work Demonstration.
主讲人：Ruixuan Liu, Chinese University of Hong Kong
主持人：（国发院）黄卓、张俊妮、孙振庭
（北大新结构经济学研究院）胡博
（经济学院）王一鸣、王熙、刘蕴霆、王法
时间：2023年11月24日（周五）10:00 AM -- 11:30 AM
地点：北京大学经济学院107
主讲人简介：
Ruixuan Liu is an associate professor from Chinese University of Hong Kong. His research interests include econometrics and data science. He has publications in Econometric Theory, Journal of Econometrics, and Quantitative Economics, among others.
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计量、金融与大数据分析workshop：Double Robust Bayesian Inference on Average Treatment Effects
发布日期：2023-11-24 12:00 来源：
题目：Double Robust Bayesian Inference on Average Treatment Effects
摘要：We study a double robust Bayesian inference procedure on the average treatment effect (ATE) under unconfoundedness. Our robust Bayesian approach involves two adjustment steps: first, we make a correction for prior distributions of the conditional mean function; second, we introduce a recentering term on the posterior distribution of the resulting ATE. We prove asymptotic equivalence of our Bayesian estimator and double robust frequentist estimators by establishing a new semiparametric Bernstein--von Mises theorem under double robustness; i.e., the lack of smoothness of conditional mean functions can be compensated by high regularity of the propensity score and vice versa. Consequently, the resulting Bayesian point estimator internalizes the bias correction as the frequentist-type doubly robust estimator, and the Bayesian credible sets form confidence intervals with asymptotically exact coverage probability. In simulations, we find that this robust Bayesian procedure leads to significant bias reduction of point estimation and accurate coverage of confidence intervals, especially when the dimensionality of covariates is large relative to the sample size and the underlying functions become complex. We illustrate our method in an application to the National Supported Work Demonstration.
主讲人：Ruixuan Liu, Chinese University of Hong Kong
主持人：（国发院）黄卓、张俊妮、孙振庭
（北大新结构经济学研究院）胡博
（经济学院）王一鸣、王熙、刘蕴霆、王法
时间：2023年11月24日（周五）10:00 AM -- 11:30 AM
地点：北京大学经济学院107
主讲人简介：
Ruixuan Liu is an associate professor from Chinese University of Hong Kong. His research interests include econometrics and data science. He has publications in Econometric Theory, Journal of Econometrics, and Quantitative Economics, among others.
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