【主讲人简介】：张政，中国人民大学长聘教授、博士生导师，统计与大数据研究院副院长，入选国家高层次青年人才计划、中国人民大学科研标兵。2011年毕业于东南大学数学系，获理学学士学位；2015年毕业于香港中文大学统计系，获统计学博士学位。2016年加入中国人民大学统计与大数据研究院，先后任助理教授、长聘副教授、长聘教授。主要研究方向为统计因果推断。已在JRSS-B, Quantitative Economics, JOE, ET, JBES, JMLR, ICML等统计学、计量经济学和机器学习领域期刊与会议发表论文20余篇，并出版英文专著1部。

【内容简介】：Estimation and inference of treatment effects under unconfounded treatment assignments often suffer from bias and the ‘curse of dimensionality’ due to the nonparametric estimation of nuisance parameters for high-dimensional confounders. Although debiased state-of-the-art methods have been proposed for binary treatments under particular treatment models, they can be unstable for small sample sizes. Moreover, directly extending them to general treatment models can lead to computational complexity. We propose a balanced neural networks weighting method for general treatment models, which leverages deep neural networks to alleviate the curse of dimensionality while retaining optimal covariate balance through calibration, thereby achieving debiased and robust estimation. Our method accommodates a wide range of treatment models, including average, quantile, distributional, and asymmetric least squares treatment effects, for discrete, continuous, and mixed treatments. Under regularity conditions, we show that our estimator achieves rate double robustness and √N-asymptotic normality, and its asymptotic variance achieves the semiparametric efficiency bound. We further develop a statistical inference procedure based on weighted bootstrap, which avoids estimating the efficient influence/score functions. Simulation results reveal that the proposed method consistently outperforms existing alternatives, especially when the sample size is small. Applications to the 401(k) dataset and the Mother’s Significant Features dataset further illustrate the practical value of the method for estimating both average and quantile treatment effects under binary and continuous treatments, respectively.

【讲座时间】：2026年10月14日（星期三）下午15:00

【讲座地点】：人文社科科研楼1801会议室