报告题目：Higher-Order Debiased Estimators for General Treatment Models

报告人：张政 副教授 中国人民大学统计与大数据研究院

报告时间：2026年4月12日 9:00-10:00

报告地点：伍卓群楼第二报告厅

校内联系人：杜明月 [email protected]

报告摘要：We have witnessed tremendous progress in developing the foundation for econometrics and causal inference in the past decades. The most popular paradigm in the current literature is the classical (first-order) semiparametric theory, in which a key building block is the (first-order) influence functions. However, it is now well known that estimators based on influence functions can be sub-optimal in terms of convergence rates in various settings. To address this issue, higher-order influence functions (HOIF) are developed, generalizing the classical semiparametric theory. However, most existing results in this regard focus on treatment effect parameters in explicit forms, such as average treatment effects (ATE). In applications, economists are often confronted with tasks of inferring more complex parameters, such as quantile treatment effects (QTE) or effects of complicated treatment regimes/policy. These more complex parameters can often only be implicitly defined as the solution to nonlinear estimating equations, which correspond to $M$/$Z$-estimation problems. Our current understanding of these problems is limited to the classical semiparametric theory. Given the foundational role of HOIF for estimating explicit parameters such as ATE, a modest step toward enriching the statistical foundation of econometrics and causal inference is to develop the corresponding higher-order estimators for those more complex parameters. To this end, we consider parameters of a class of non-separable structural models in the econometrics literature and develop a class of higher-order estimators for the target parameters. Statistical properties of these higher-order estimators are derived by leveraging recent advances in $U$-processes theory. Our proposed higher-order estimators relax complexity-reducing assumptions, quantified via \Holder{} smoothness, on the nuisance parameters, compared to existing estimators for many important parameters in this class, including QTE and quantile dose-response functions. Numerical experiments, including simulation studies and a real data analysis, are also conducted to corroborate our theoretical claims and illustrate how higher-order estimators can be used in practice.

报告人简介：张政，中国人民大学长聘副教授、研究员、博士生导师，统计与大数据研究院副院长，入选国家级青年人才计划。长期致力于统计因果推断的理论方法创新及其在公共政策评估等领域的应用研究。在JRSS-B，JOE，Quantitative Economics，Journal of Machine Learning Research等期刊发表论文20余篇。