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| Main Author: | |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.10946 |
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| _version_ | 1866908637348233216 |
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| author | Boussim, Onil |
| author_facet | Boussim, Onil |
| contents | This paper provides a nonparametric framework for causal inference with categorical outcomes under binary treatment and binary instrument settings. I 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 \emph{association similarity}, which requires the dependence between unobserved factors driving treatment and potential outcomes to be invariant across treatment states, I achieve point identification of the full distribution of potential outcomes. Recognizing that this assumption may be strong in some contexts, I 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. I illustrate the framework in an empirical application, estimating the causal effect of private health insurance on health outcomes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_10946 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Identifying treatment effects on categorical outcomes in IV models Boussim, Onil Econometrics This paper provides a nonparametric framework for causal inference with categorical outcomes under binary treatment and binary instrument settings. I 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 \emph{association similarity}, which requires the dependence between unobserved factors driving treatment and potential outcomes to be invariant across treatment states, I achieve point identification of the full distribution of potential outcomes. Recognizing that this assumption may be strong in some contexts, I 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. I illustrate the framework in an empirical application, estimating the causal effect of private health insurance on health outcomes. |
| title | Identifying treatment effects on categorical outcomes in IV models |
| topic | Econometrics |
| url | https://arxiv.org/abs/2510.10946 |