主题：Double Robust Bayesian Inference on Average Treatment Effects
主讲人：刘睿轩 香港中文大学
主持人：柯书尧 太阳城集团
时间：2024年4月19日（周五）上午10:00-11:30
地点：太阳城集团石牌校区tyc1286太阳成集团大楼（中惠楼）503室
摘要
We propose a double robust Bayesian inference procedure on the average treatment effect (ATE) under unconfoundedness. Our robust Bayesian approach involves two important modifications: first, we adjust the prior distributions of the conditional mean function; second, we correct the posterior distribution of the resulting ATE. Both adjustments make use of pilot estimators motivated by the semiparametric influence function for ATE estimation. We prove asymptotic equivalence of our Bayesian procedure and efficient frequentist ATE 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 credible sets form confidence intervals with asymptotically exact coverage probability. In simulations, our double robust Bayesian procedure leads to significant bias reduction of point estimation over conventional Bayesian methods and more accurate coverage of confidence intervals compared to existing frequentist methods. We illustrate our method in an application to the National Supported Work Demonstration.
主讲人介绍
刘睿轩，香港中文大学商学院副教授。研究领域包括半参数与非参数估计、微观计量模型、贝叶斯统计等，研究工作发表于Journal of Econometrics, Econometric Theory, Quantitative Economics等国际计量经济学顶级期刊；担任Journal of Royal Statistical Society (Series B), Journal of the American Statistical Association, Journal of Econometrics等国际知名统计学与计量经济学期刊审稿人；并主持香港The General Research Fund (GRF)研究基金。
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校对|柯书尧
责编|彭毅
初审|姜云卢
终审发布|何凌云
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