【数学与统计及交叉学科前沿论坛------高端学术讲座第163场】
报告题目：Design-based causal inference in bipartite experiments
报 告 人：丁鹏
报告时间：8月12日周二15:30-16:30
报告地点：阜成路西校区综合楼1215A
报告摘要：Bipartite experiments arise in various fields, in which the treatments are randomized over one set of units, while the outcomes are measured over another separate set of units. However, existing methods often rely on strong model assumptions about the data-generating process. Under the potential outcomes formulation, we explore design-based causal inference in bipartite experiments under weak assumptions by leveraging the sparsity structure of the bipartite graph that connects the treatment units and outcome units. We make several contributions. First, we formulate the causal inference problem under the design- based framework that can account for the bipartite interference. Second, we propose a consistent point estimator for the total treatment effect, a policy-relevant parameter that measures the difference in the outcome means if all treatment units receive the treatment or control. Third, we establish a central limit theorem for the estimator and propose a conservative variance estimator for statistical inference. Fourth, we discuss a covariate adjustment strategy to enhance estimation efficiency.
报告人简介：丁鹏，加州大学伯克利分校统计系副教授。2008年、2011年先后获得北京大学学士，硕士学位，2015年获得哈佛大学统计学博士学位，主要研究方向为因果推断、缺失数据等。2016年获美国统计学会流行病学分会青年研究员奖，2017年获世界华人数学家大会（ICCM）最佳论文奖，2018年凭借在因果推断方面的方法论贡献获皇家统计学会（RSS）Guy铜奖，2020年获英国国家科学基金会CAREER奖，2023年获得数理统计学会IMS颁发的COPSS Emerging Leaders。在《Econometrica》、JASA、《Biometrika》等顶级期刊发表论文60余篇。现任《Biometrika》、JASA等期刊编委，组织多场国际学术会议。
