主讲人：周前坤

时间：2025年6月9日9：30

地点：文昌校区第二教学楼306

报告摘要：This article studies causal inference with social interactions in a non-experimental setting with a non-staggered binary treatment. We characterise the potential outcomes by a factor model that allows for interference between any two units. Under this specification, the observed outcomes can be represented by a structural break model and the treatment effects are exactly the outcome changes induced by this break. Since the structural break literature has not yet provided any estimator for such an estimand, we propose an innovative estimation procedure for treatment effects. Under standard assumptions, the estimator of every individual and time specific treatment effect is proved to be consistent and asymptotically normal as the numbers of units, pre-treatment and post-treatment times go to infinity. We find consistent estimators for the asymptotic variances, which enables asymptotically pivotal inference on treatment effects. As a by-product of causal inference, we contribute to the structural break literature by providing a valid approach to the estimation and inference of outcomes changes induced by a structural break. Furthermore, we extend our method to models with covariates. Finally, we investigate the performances of the proposed method in finite samples by Monte Carlo experiments and an empirical application with real data.

主讲人简介：周前坤，现任美国路易斯安那州立大学经济学教授和Thomas Singletary Business Partnership 荣誉教授，分别在北京大学和美国南加州大学获得硕士和博士学位。主要研究方向包括面板数据模型、非参数半参数计量模型、金融计量经济学、大数据分析和处理效应评估等。在Journal of Econometrics，Journal of Business and Economic Statistics, Journal of Applied Econometrics, Econometric Theory等国际权威期刊发表高水平论文30多篇，同时担任上述期刊的匿名审稿人。