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arXiv Machine Learning

Reliable Selection of Heterogeneous Treatment Effect Estimators

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.

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arXiv Machine Learning
Sep 23

Conditional Distributional Treatment Effects: Doubly Robust Estimation and Testing

The paper introduces a new estimand for conditional distributional treatment effects that captures how treatments influence the entire outcome distribution, including variance and tail risks, in a covariate-dependent manner. It presents a doubly robust estimator that is minimax optimal locally and uses it to construct a test for global homogeneity of conditional potential outcome distributions. The test accommodates discrepancies beyond the maximum mean discrepancy, guarantees valid type‑1 error, is consistent against fixed alternatives, and includes a computationally efficient, permutation‑free algorithm with exact closed‑form expressions for two natural discrepancies.

By Saksham Jain, Alex Luedtke
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arXiv Machine Learning
Jun 4

Orthogonal Learner for Estimating Heterogeneous Long-Term Treatment Effects

arXiv:2604. 00915v2 Announce Type: replace Abstract: Estimation of heterogeneous long-term treatment effects (HLTEs) is relevant for personalized decision-making in marketing, economics, and medicine, where short-term observational datasets are often combined with long-term observational datasets.

By Haorui Ma, Dennis Frauen, Valentyn Melnychuk, Stefan Feuerriegel
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arXiv Statistics ML
Aug 24

Double Machine Learning of Continuous Treatment Effects with Additive Instrumental Variables

The paper introduces a new framework for identifying average dose-response functions in the presence of unmeasured confounding by using instrumental variables. It defines a uniform regular weighting function and partitions the treatment space into open sets where local identification is possible. For estimation, the authors propose an augmented inverse probability weighted score within a debiased machine learning setting, along with practical guidance for constructing weighting functions, falsification tests for the additive IV condition, and asymptotic theory for kernel regression or empirical risk minimization estimators.

By Shuyuan Chen, Peng Zhang, Yifan Cui
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arXiv Machine Learning
Jun 8

Automatic, Debiased, and Invariant Counterfactual Generation under General Interventions

arXiv:2606. 07399v1 Announce Type: cross Abstract: Generative models for counterfactual outcomes have great potential to support decision-making under complex interventions, but existing approaches are limited by unstable estimation, poor generalization across environments, and bias from nuisance model misspecification.

By Raphael C Kim, Jingsen Zhu, Ramin Zabih, Michele Santacatterina
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