Parametric models for principal causal effects produce only partial identification without principal ignorability, with association parameters for strata identifiable solely under violation of that assumption plus strong parametric constraints.
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Derives the nonparametric efficient influence function for transported distributional and quantile treatment effects, using surrogates only for efficiency gains under missing-at-random primary outcomes to produce a cross-fitted one-step estimator with asymptotic guarantees.
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Partial identification of principal causal effects under violations of principal ignorability
Parametric models for principal causal effects produce only partial identification without principal ignorability, with association parameters for strata identifiable solely under violation of that assumption plus strong parametric constraints.
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Efficient Transported Distributional and Quantile Treatment Effects with Surrogate-Assisted Missing Primary Outcomes
Derives the nonparametric efficient influence function for transported distributional and quantile treatment effects, using surrogates only for efficiency gains under missing-at-random primary outcomes to produce a cross-fitted one-step estimator with asymptotic guarantees.