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arXiv Machine Learning By Jiayi Guo, Zijun Gao

Reliable Selection of Heterogeneous Treatment Effect Estimators

Read the original on arXiv Machine Learning →

The paper introduces a method for selecting the best heterogeneous treatment effect (HTE) estimator from a set of candidates when the true treatment effect is unobserved. It frames estimator selection as a multiple testing problem and proposes a cross‑fitted, exponentially weighted test statistic that uses a two‑way sample splitting scheme to separate nuisance estimation from weight learning, ensuring stability for inference. The authors prove asymptotic familywise error rate control under mild conditions and demonstrate empirically that their procedure reduces false selections compared to common methods on ACIC 2016, IHDP, and Twins benchmarks.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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