Paul Diegert, Matthew A. Masten, Alexandre Poirier
arXiv 8 Apr 2026 · Econometrics
arXiv:2604.07604 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Gilchrist, D. S. and E. G. Sands (2016) Something to Talk About: Social Spillovers in Movie Consumption | 0.874 | 8 | 2 | 100% |
| 2 | Manski, C. F (1990) Nonparametric bounds on treatment effects | 0.874 | 6 | 2 | 100% |
| 3 | Balke, A. and J. Pearl (1997) Bounds on treatment effects from studies with imperfect compliance | 0.874 | 5 | 2 | 100% |
| 4 | Kitagawa, T (2021) The identification region of the potential outcome distributions under instrument independence | 0.811 | 4 | 2 | 100% |
| 5 | Kline, P. and A. Santos (2013) Sensitivity to missing data assumptions: Theory and an evaluation of the US wage structure | 0.644 | 2 | 2 | 100% |
| 6 | Manski, C. F (1983) Closest empirical distribution estimation | 0.644 | 2 | 2 | 100% |
| 7 | Masten, M. A. and A. Poirier (2018) Identification of treatment effects under conditional partial independence self | 0.644 | 2 | 2 | 100% |
| 8 | Masten, M. A. and A. Poirier (2021) Salvaging falsified instrumental variable models self | 0.644 | 2 | 2 | 100% |
| 9 | Mellon, J (2025) Rain, Rain, Go Away: 194 Potential Exclusion-Restriction Violations for Studies Using Weather as an Instrumental Variable | 0.644 | 2 | 2 | 100% |
| 10 | Pearl, J (1995) On the testability of causal models with latent and instrumental variables, in | 0.644 | 2 | 2 | 100% |
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