Copula parameterization of potential outcome dependence enables point identification, rate-doubly-robust estimation, and sensitivity analysis for causal effects with ordinal outcomes under unconfoundedness.
Journal of the Royal Statistical Society Series B: Statistical Methodology , volume=
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The MQIV model identifies the ATT via a modified Wald ratio under a multiplicative treatment model that permits direct effects of the quasi-instrument on the outcome.
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Causal inference with ordinal outcomes: copula-based identification, estimation and sensitivity analysis
Copula parameterization of potential outcome dependence enables point identification, rate-doubly-robust estimation, and sensitivity analysis for causal effects with ordinal outcomes under unconfoundedness.
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The Multiplicative Quasi-Instrumental Variable Model
The MQIV model identifies the ATT via a modified Wald ratio under a multiplicative treatment model that permits direct effects of the quasi-instrument on the outcome.