Identifying treatment effects on categorical outcomes in IV models

Onil Boussim

Papers from arXiv.org

Abstract: This paper studies population treatment effects when the outcome is unordered categorical, the treatment is binary, and the instrument is binary. I introduce an assumption called association similarity. For each instrument value, association similarity requires the odds-ratio association between potential treatment and potential outcome to be the same whether the outcome is evaluated under treatment or under no treatment. Under association similarity and a testable categorywise dominance condition, the full potential-outcome distributions are point identified. The identification is constructive and allows estimation using sample frequencies. I also consider weaker restrictions on departures from association similarity that lead to sharp partial identification. I illustrate the method with an application about the effect of insurance on health outcomes.

Date: 2025-10, Revised 2026-10
New Economics Papers: this item is included in nep-ecm
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