Random Assignment with Nonrandom Peers: A Structural Approach to Counterfactual Treatment Assessment
针对同伴选择内生性导致分配干预效果不佳的问题，构建了一个两阶段模型：先通过连续链接决策形成网络，再基于网络决定结果。利用印度随机实验数据估计模型，评估样本外预测表现，并模拟优先分配规则下的结果。
Abstract Efforts to leverage peer effects by changing assignment have often fallen short due to endogenous peer choice. To address this, I build a two-part model: agents form networks via continuous linking decisions; conditional on realized networks, outcomes are determined. I provide results on identification of both parts of the model. Using data from a randomized study in India, I estimate the model, assess its performance in out-of-sample prediction, and simulate outcomes under preferential assignment rules. This paper contributes new methodology for identifying effects of alternative assignments in the presence of network endogeneity, as well as identification of network formation models.