Exclusion and exogeneity are core assumptions in instrumental variable (IV) analyses, but their empirical validity is often debated. This paper develops new sensitivity analyses for these assumptions. Our results accommodate arbitrary heterogeneity in treatment effects and do not impose any monotonicity requirements on the first stage. Specifically, we derive identified sets for the marginal distributions of potential outcomes and their functionals, like average treatment effects, under a broad class of nonparametric relaxations of the exclusion and exogeneity assumptions. These identified sets are characterized as solutions to linear programs and have desirable theoretical properties. We explain how to estimate these solutions using computationally tractable methods even when the linear program is infinite-dimensional. We illustrate these methods with an empirical application to peer effects in movie viewership, using weather as a potentially imperfect instrument.

翻译：排除外生性与工具变量（IV）分析中的核心假设——排除限制和外生性——其实证有效性常存争议。本文针对这些假设提出了新的敏感性分析方法。研究结果允许处理效应存在任意异质性，且不要求一阶段具备任何单调性条件。具体而言，我们在排除限制和外生性假设的广义非参数放松条件下，推导出潜在结果边际分布及其泛函（如平均处理效应）的识别集。这些识别集被刻画为线性规划解，并具有理想的理论性质。我们阐释了即使在线性规划为无限维情形下，如何通过计算可处理方法估计这些解。通过将天气作为潜在不完美工具，我们以电影观影的同伴效应实证研究展示了这些方法的应用。