题目：Randomization Tests for Weak Null Hypotheses

主讲人：加州大学伯克利分校 丁鹏助理教授

主持人：西南财经大学统计学院 常晋源教授

时间：2020年6月26日（星期五）11:00-12:20

直播平台及会议ID：腾讯会议，499 538 817

报告摘要：

The Fisher randomization test (FRT) is appropriate for any test statistic, under a sharp null hypothesis that can recover all missing potential outcomes. However, it is often sought after to test a weak null hypothesis that the treatment does not affect the units on average. To use the FRT for a weak null hypothesis, we must address two issues. First, we need to impute the missing potential outcomes although the weak null hypothesis cannot determine all of them. Second, we need to choose a proper test statistic. For a general weak null hypothesis, we propose an approach to imputing missing potential outcomes under a compatible sharp null hypothesis. Building on this imputation scheme, we advocate a studentized statistic. The resulting FRT has multiple desirable features. First, it is model-free. Second, it is finite-sample exact under the sharp null hypothesis that we use to impute the potential outcomes. Third, it conservatively controls large-sample type I error under the weak null hypothesis of interest. Therefore, our FRT is agnostic to the treatment effect heterogeneity. We establish a unified theory for general factorial experiments and extend it to stratified and clustered experiments. We also propose a general strategy for covariate-adjusted FRTs.

主讲人简介：

丁鹏，现任加州大学伯克利分校统计系助理教授，曾于2004到2011从北京大学取得数学和经济学学士，统计学硕士，于2011到2015从哈佛大学取得统计学博士，而后在哈佛大学流行病学做博士后研究。其主要研究方向为因果推断、缺失数据和实验设计。