Inference on Local Variable Importance Measures for Heterogeneous Treatment Effects

• URL: http://arxiv.org/abs/2510.18843v1
• Date: Tue, 21 Oct 2025 17:35:33 GMT
• Title: Inference on Local Variable Importance Measures for Heterogeneous Treatment Effects
• Authors: Pawel Morzywolek, Peter B. Gilbert, Alex Luedtke,
• Abstract summary: We provide an inferential framework to assess variable importance for heterogeneous treatment effects.<n>This assessment is especially useful in high-risk domains such as medicine.
• Score: 1.121518046252855
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We provide an inferential framework to assess variable importance for heterogeneous treatment effects. This assessment is especially useful in high-risk domains such as medicine, where decision makers hesitate to rely on black-box treatment recommendation algorithms. The variable importance measures we consider are local in that they may differ across individuals, while the inference is global in that it tests whether a given variable is important for any individual. Our approach builds on recent developments in semiparametric theory for function-valued parameters, and is valid even when statistical machine learning algorithms are employed to quantify treatment effect heterogeneity. We demonstrate the applicability of our method to infectious disease prevention strategies.

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