光华讲坛——社会名流与企业家论坛第 期

主 题：Causal inference in network experiments: regression-based analysis and design-based properties网络实验中的因果推理:基于回归分析和基于设计的性质

主讲人：加州大学伯克利分校丁鹏副教授

主持人：统计学院林华珍教授

时间：2023年12月29日（周五）下午16:00-17:00

举办地点：柳林校区弘远楼408会议室

主办单位：统计研究中心和统计学院 科研处

主讲人简介：

I am an Associate Professor in the Department of Statistics, UC Berkeley. I obtained my Ph.D. from the Department of Statistics, Harvard University in May 2015, and worked as a postdoctoral researcher in the Department of Epidemiology, Harvard T. H. Chan School of Public Health until December 2015. Previously, I received my B.S. (Mathematics), B.A. (Economics), and M.S. (Statistics) from Peking University.

丁鹏，加州大学伯克利分校统计系副教授。2015年5月获得哈佛大学统计系博士学位，2015年5月-12月在哈佛大学陈曾熙公共卫生学院流行病学系从事博士后研究工作。此前，在北京大学获得了数学学士学位、经济学学士学位和统计学硕士学位。

内容简介：

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.

研究单位间的干扰或溢出效应是许多社会科学问题的中心任务。网络实验是这项任务的有力工具，它通过在网络上随机分配处理方法来避免内生性。然而，在不强加强建模假设的情况下正确分析网络实验是非常重要的。在此之前，许多研究者提出了复杂的网络实验因果效应的点估计和标准误差。主讲人进一步表明，如果仔细指定回归函数和鲁棒标准误差以适应网络实验下的干扰模式，基于回归的点估计器和标准误差可以有很强的理论保证。主讲人首先回顾一个众所周知的结果，即Hajek估计量在数值上与基于暴露映射逆概率的加权最小二乘拟合的系数相同。此外，主讲人证明了基于回归的方法具有三个显著的优点:易于实现，通过相同的加权最小二乘拟合获得标准误差的能力，以及将协变量集成到分析中的能力，从而提高了估计效率。进一步，主讲人分析了基于回归的网络鲁棒标准误差的渐近偏差。认识到协方差估计量可能是反保守的，主讲人提出了一个调整的协方差估计量来提高经验覆盖率。虽然主讲人关注的是基于回归的点估计量和标准误差，但主讲人的理论在基于设计的框架下成立，该框架假设随机性完全来自网络实验的设计，并允许任意错误规范回归模型。