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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Derives new analytical sample size formulas for the marginal hazard ratio under IPW estimation in Cox models, correcting classic log-rank formulas for RCTs and adding an overlap measure for observational data.
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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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Sample size and power calculations for causal inference with time-to-event outcomes
Derives new analytical sample size formulas for the marginal hazard ratio under IPW estimation in Cox models, correcting classic log-rank formulas for RCTs and adding an overlap measure for observational data.