报告题目：Causal inference in network experiments: regression-based analysis and design-based properties
报 告 人：丁鹏 副教授 加州大学伯克利分校
报告时间：2023年12月19日 10:30-11:30
报告地点：数学楼一楼第一报告厅
校内联系人：杜明月 mingydu@jlu.edu.cn
报告摘要：Investigating interference or spillover effects among units is a central task in many social science problems. Network experiments are powerful tools for this task, which avoids endogeneity by randomly assigning treatments to units over networks. However, it is non-trivial to analyze network experiments properly without imposing strong modeling assumptions. Previously, many researchers have proposed sophisticated point estimators and standard errors for causal effects under network experiments. We further show that regression-based point estimators and standard errors can have strong theoretical guarantees if the regression functions and robust standard errors are carefully specified to accommodate the interference patterns under network experiments. We first recall a well-known result that the Hajek estimator is numerically identical to the coefficient from the weighted-least-squares fit based on the inverse probability of the exposure mapping. Moreover, we demonstrate that the regression-based approach offers three notable advantages: its ease of implementation, the ability to derive standard errors through the same weighted-least-squares fit, and the capacity to integrate covariates into the analysis, thereby enhancing estimation efficiency. Furthermore, we analyze the asymptotic bias of the regression-based network-robust standard errors. Recognizing that the covariance estimator can be anti-conservative, we propose an adjusted covariance estimator to improve the empirical coverage rates. Although we focus on regression-based point estimators and standard errors, our theory holds under the design-based framework, which assumes that the randomness comes solely from the design of network experiments and allows for arbitrary misspecification of the regression models.
报告人简介：丁鹏，2004-2011年就读于北京大学，获得数学学士、经济学学士以及统计学硕士学位，2015年于哈佛大学获得博士学位，后于哈佛大学公共卫生学院做博士后研究员。现为美国加州大学伯克利分校统计系副教授。
