标题： 网络倾向得分：溢出效应、同质性与治疗选择 显示英文标题

标题： The Network Propensity Score: Spillovers, Homophily, and Selection into Treatment

摘要： 我建立了一致模型中无混淆性的原始条件，该模型具有异质处理效应、溢出效应、可观测变量选择和网络形成。 我在最小可交换性条件下识别平均部分效应。 如果社会互动也是匿名的，我推导出一个三维网络倾向得分，描述其支持条件，将其与最近的网络伪度量研究联系起来，并进行扩展研究。 我提出了一种随机系数模型的两步半参数估计量，当网络的数量和规模增长时，该估计量是一致且渐近正态的。 我将我的估计量应用于乌干达的政治参与干预和印度的微金融应用。 显示英文摘要

摘要： I establish primitive conditions for unconfoundedness in a coherent model that features heterogeneous treatment effects, spillovers, selection-on-observables, and network formation. I identify average partial effects under minimal exchangeability conditions. If social interactions are also anonymous, I derive a three-dimensional network propensity score, characterize its support conditions, relate it to recent work on network pseudo-metrics, and study extensions. I propose a two-step semiparametric estimator for a random coefficients model which is consistent and asymptotically normal as the number and size of the networks grows. I apply my estimator to a political participation intervention Uganda and a microfinance application in India.