标题： 在IV模型中识别分类结果的处理效应 显示英文标题

标题： Identifying treatment effects on categorical outcomes in IV models

摘要： 本文提供了一个非参数框架，用于在二元处理和二元工具变量设置下进行分类结果的因果推断。 我们将观察到的结果和处理的联合概率分解为潜在结果和处理的边缘概率，以及捕捉由于未观测异质性导致的选择偏差的关联参数。 在一种新的识别假设——关联相似性下，该假设要求未观测因素与潜在结果之间的依赖关系在处理状态之间保持不变，我们实现了潜在结果完整分布的点识别。 认识到在某些情况下这一假设可能过于强烈，我们提出了两种较弱的替代方案：单调关联，它限制了选择异质性的方向；以及有界关联，它限制了其幅度。 这些放松的假设提供了尖锐的部分识别界限，将点识别作为特殊情况，并促进了透明的敏感性分析。 我们在一个实证应用中展示了该框架，估计了私人健康保险对健康结果的因果效应。 显示英文摘要

摘要： This paper provides a nonparametric framework for causal inference with categorical outcomes under binary treatment and binary instrument settings. We decompose the observed joint probability of outcomes and treatment into marginal probabilities of potential outcomes and treatment, and association parameters that capture selection bias due to unobserved heterogeneity. Under a novel identifying assumption, association similarity, which requires the dependence between unobserved factors and potential outcomes to be invariant across treatment states, we achieve point identification of the full distribution of potential outcomes. Recognizing that this assumption may be strong in some contexts, we propose two weaker alternatives: monotonic association, which restricts the direction of selection heterogeneity, and bounded association, which constrains its magnitude. These relaxed assumptions deliver sharp partial identification bounds that nest point identification as a special case and facilitate transparent sensitivity analysis. We illustrate the framework in an empirical application, estimating the causal effect of private health insurance on health outcomes.