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.
arXiv preprint arXiv:2411.03489 , year=
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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.