Abstract:
Modern causal inference increasingly integrates flexible machine learning, optimization, and graph-based discovery methods. However, standard inferential procedures typically rely on regularity conditions, such as asymptotic normality, that often fail in realistic, data-driven, or high-dimensional settings. In this talk, we provide a unified framework for nonregular inference, where estimators deviate from classical asymptotic behavior, and we introduce practical methods for valid uncertainty quantification even under slow nuisance convergence, weak identification, or data-dependent selection. In particular, we highlight how our perturbed inference approach can restore valid coverage by injecting small perturbations that yield reliable confidence sets without requiring classical asymptotic normality. In this talk, I will focus on Perturbed Double Machine Learning and Distributionally Robust Synthetic Control.
About the Speaker:
浙江大学数据科学研究中心求是讲席教授，入选国家高层次人才计划。2012年获香港中文大学学士学位，2017年获宾夕法尼亚大学统计学博士学位，师从著名统计学家、COPSS奖获得者蔡天文教授。2017—2025年在美国罗格斯大学统计系任教，历任助理教授、终身副教授，2025年加入浙江大学。主要研究方向包括因果推断、高维统计、多源学习与分布鲁棒优化、非正则统计推断，以及统计与优化的交叉方法。
Your participation is warmly welcomed!
欢迎扫码关注北大统计科学中心公众号，了解更多讲座信息!
