{"ID":2852374,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.18843","arxiv_id":"2510.18843","title":"Inference on Variable Importance for Treatment Effect Heterogeneity: Shapley Values and Beyond","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.","short_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 loc...","url_abs":"https://arxiv.org/abs/2510.18843","url_pdf":"https://arxiv.org/pdf/2510.18843v2","authors":"[\"Pawel Morzywolek\",\"Peter B. Gilbert\",\"Alex Luedtke\"]","published":"2025-10-21T17:35:33Z","proceeding":"stat.ME","tasks":"[\"stat.ME\",\"math.ST\",\"stat.ML\"]","methods":"[]","has_code":false}