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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Establishes CMMD as a family of kernel-based metrics for differences between conditional distributions, with levels 0-2 and general s, plus a doubly robust estimator consistent if at least one model is correct.
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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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Measuring Differences between Conditional Distributions using Kernel Embeddings
Establishes CMMD as a family of kernel-based metrics for differences between conditional distributions, with levels 0-2 and general s, plus a doubly robust estimator consistent if at least one model is correct.