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网络干扰下因果推断的局部方法AI 解读

We propose a new nonparametric modeling framework for causal inference when outcomes depend on how agents are linked in a social or economic network. Such network interference describes a large literature on treatment spillovers, social interactions, social learning, information diffusion, disease and financial contagion, social capital formation, and more. Our approach works by first characterizing how an agent is linked in the network using the configuration of other…

对《暴露映射误设下的因果推断：区分定义与假设》的讨论：政策评价局限AI 解读

We thank Sävje (2024) for a thought-provoking article and appreciate the opportunity to share our perspective as social scientists. In his article, Sävje recommended misspecified exposure effects as a way to avoid strong assumptions about interference when analysing the results of an experiment. In this discussion, we highlight a key limitation of Sävje’s recommendation. Exposure effects are not generally useful for evaluating social policies without the strong assumptions…

随机网络结构差异检验AI 解读

How can one determine whether a treatment, such as the introduction of a social program or trade shock, alters agents' incentives to form links in a network? This paper proposes analogs of a two‐sample Kolmogorov–Smirnov test, widely used in the literature to test the null hypothesis of no treatment effects, for network data. It first specifies a testing problem in which the null hypothesis is that two networks are drawn from the same random graph model. It then describes…

利用网络数据识别与估计部分线性回归模型AI 解读

I study a regression model in which one covariate is an unknown function of a latent driver of link formation in a network. Rather than specify and fit a parametric network formation model, I introduce a new method based on matching pairs of agents with similar columns of the squared adjacency matrix, the ij th entry of which contains the number of other agents linked to both agents i and j . The intuition behind this approach is that for a large class of network formation…