A doubly cross-fit DR sieve learner identifies and estimates within-stratum heterogeneous treatment effects under principal ignorability and odds-ratio sensitivity, with oracle rates and uniform bands.
arXiv preprint arXiv:2208.00872 , year=
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Doubly cross-fit debiased machine learning of heterogeneous treatment effects under principal stratification
A doubly cross-fit DR sieve learner identifies and estimates within-stratum heterogeneous treatment effects under principal ignorability and odds-ratio sensitivity, with oracle rates and uniform bands.