主講人：周前坤

時間：2025年6月9日9：30

地點：文昌校區(qū)第二教學樓306

報告摘要：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.

主講人簡介：周前坤，現(xiàn)任美國路易斯安那州立大學經(jīng)濟學教授和Thomas Singletary Business Partnership 榮譽教授，分別在北京大學和美國南加州大學獲得碩士和博士學位。主要研究方向包括面板數(shù)據(jù)模型、非參數(shù)半?yún)?shù)計量模型、金融計量經(jīng)濟學、大數(shù)據(jù)分析和處理效應評估等。在Journal of Econometrics，Journal of Business and Economic Statistics, Journal of Applied Econometrics, Econometric Theory等國際權威期刊發(fā)表高水平論文30多篇，同時擔任上述期刊的匿名審稿人。