Onil Boussim
arXiv 13 Oct 2025 · Econometrics
arXiv:2510.10946 · PDF · Extracted main text
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.
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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 | Han, Sukjin and Lee, Sungwon (2019) Estimation in a generalization of bivariate probit models with dummy endogenous regressors | 0.644 | 2 | 2 | 100% |
| 2 | Manski, Charles F (1990) Nonparametric bounds on treatment effects | 0.644 | 2 | 2 | 100% |
| 3 | Acerenza, Santiago and Bartalotti, Otávio and Kédagni, Désiré (2023) Testing identifying assumptions in bivariate probit models | 0.405 | 1 | 1 | 100% |
| 4 | Angrist, Joshua and Imbens, Guido (1995) Identification and estimation of local average treatment effects | 0.405 | 1 | 1 | 100% |
| 5 | Balke, Alexander and Pearl, Judea (1997) Bounds on treatment effects from studies with imperfect compliance | 0.405 | 1 | 1 | 100% |
| 6 | Chernozhukov, Victor and Fernández-Val, Iván and Han, Sukjin and Wüt… (2024) Estimating Causal Effects of Discrete and Continuous Treatments with Binary Instruments | 0.405 | 1 | 1 | 100% |
| 7 | Dubin, Jeffrey A and McFadden, Daniel L (1984) An econometric analysis of residential electric appliance holdings and consumption | 0.405 | 1 | 1 | 100% |
| 8 | Dubin, Jeffrey A and Rivers, Douglas (1989) Selection bias in linear regression, logit and probit models | 0.405 | 1 | 1 | 100% |
| 9 | Freedman, David A and Sekhon, Jasjeet S (2010) Endogeneity in probit response models | 0.405 | 1 | 1 | 100% |
| 10 | Han, Sukjin and Vytlacil, Edward J (2017) Identification in a generalization of bivariate probit models with dummy endogenous regressors | 0.405 | 1 | 1 | 100% |
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