Proposes a doubly cross-fit doubly robust machine learner for conditional principal causal effects under principal ignorability with odds ratio sensitivity, with limit theory and application to an acute lung injury trial.
Jiang , Zhichao Z
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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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Learning heterogeneous treatment effects under principal stratification
Proposes a doubly cross-fit doubly robust machine learner for conditional principal causal effects under principal ignorability with odds ratio sensitivity, with limit theory and application to an acute lung injury trial.
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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.