AI 中文总结
本文提出含溢出的罗伊模型，开发贝叶斯推断算法，将其应用于美国机会区计划，发现该计划对住房发展有正向直接效应但溢出收益有限，扩张回报递减。
AI 中文摘要
本文开发了一种新的计量经济学框架，用于在存在内生处理选择和单一大型网络或空间环境中溢出效应的情境下识别和估计政策相关的因果效应。依赖于无混淆性或无干扰假设的传统因果推断方法在这些场景中通常不适用。我们引入了一种溢出罗伊模型（Spillover Roy model），该模型联合建模内生处理选择和潜在结果，同时通过邻居处理的低维暴露映射允许溢出效应。该模型捕捉了对处理的潜在抵抗程度和邻居暴露水平上的异质性处理响应。在该框架内，我们定义了可行政策变化下政策相关的直接效应、溢出效应和总效应，并表明总效应可分解为政策诱导参与带来的直接成分和政策诱导邻居处理暴露变化带来的溢出成分。对于估计和推断，我们开发了一种带参数扩展的贝叶斯数据增强算法，该算法能够对异质性因果效应和政策反事实进行高效的后验计算和一致的不确定性量化。对美国机会区（U.S. Opportunity Zones）计划的应用发现，其对住房发展有正向直接效应，但溢出收益有限，而反事实政策分析显示该计划扩张的回报递减。
英文摘要
This paper develops a new econometric framework to identify and estimate policy-relevant causal effects in contexts with endogenous selection into treatment and spillovers within single large networks or spatial settings. Conventional causal inference methods relying on either unconfoundedness or no-interference assumptions are generally inadequate in these scenarios. We introduce a Spillover Roy model that jointly models endogenous treatment selection and potential outcomes while allowing spillovers through a low-dimensional exposure mapping of neighbors' treatments. The model captures heterogeneous treatment responses across levels of latent resistance to treatment and neighborhood exposure. Within this framework, we define policy-relevant direct, spillover, and total effects under feasible policy changes and show that the total effect decomposes into a direct component from policy-induced participation and a spillover component from policy-induced changes in neighborhood treatment exposure. For estimation and inference, we develop a Bayesian data-augmentation algorithm with parameter expansion that enables efficient posterior computation and coherent uncertainty quantification for heterogeneous causal effects and policy counterfactuals. An application to the U.S. Opportunity Zones program finds positive direct effects on housing development but limited spillover benefits, while counterfactual policy analysis reveals diminishing returns from program expansion.
