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 authors develop and benchmark inference methods for win statistics in cluster-randomized trials with composite endpoints, filling a gap in handling within-cluster dependence.
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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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Statistical inference with win statistics in cluster-randomized trials with composite outcomes
The authors develop and benchmark inference methods for win statistics in cluster-randomized trials with composite endpoints, filling a gap in handling within-cluster dependence.