A Conditional Distribution Equality Testing Framework using Deep Generative Learning

主讲人：王桐

王桐，东南大学统计与数据科学学院教授，国家青年高层次人才。于2023年在香港中文大学理学院统计系获得博士学位。之后分别在美国耶鲁大学公共卫生学院生物统计系与新加坡国立大学工业工程与管理系，担任博士后职位。主要研究兴趣聚焦于统计推断、深度学习、 生成学习与半监督学习等统计学、 风险管理与人工智能交叉领域的研究。

1  报告信息

时间 

2026年6月12日(周五)

10:00

地点 

中国人民大学通州校区经济学部楼215会议室

2  报告摘要

In this paper, we propose a general framework for testing the conditional distribution equality in a two-sample problem, which is most relevant to covariate shift and causal discovery. Our framework is built on neural network-based generative methods and sample splitting techniques by transforming the conditional testing problem into an unconditional one. We introduce the generative classification accuracy-based conditional distribution equality test (GCA-CDET) to illustrate the proposed framework. We establish the convergence rate for the learned generator by deriving new results related to the recently-developed offset Rademacher complexity and prove the testing consistency of GCA-CDET under mild conditions. Empirically, we conduct numerical studies including synthetic datasets and two real-world datasets, demonstrating the effectiveness of our approach. Additional discussions on the optimality of the proposed framework are provided in the online supplementary material.