報告題目：Causal inference in network experiments: regression-based analysis and design-based properties
報 告 人：丁鵬 副教授 加州大學伯克利分校
報告時間：2023年12月19日 10:30-11:30
報告地點：數學樓一樓第一報告廳
校内聯系人：杜明月 mingydu@jlu.edu.cn
報告摘要：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.
報告人簡介：丁鵬，2004-2011年就讀于北京大學，獲得數學學士、經濟學學士以及統計學碩士學位，2015年于哈佛大學獲得博士學位，後于哈佛大學公共衛生學院做博士後研究員。現為美國加州大學伯克利分校統計系副教授。
