Inference on Variable Importance for Treatment Effect Heterogeneity: Shapley Values and Beyond
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 26 of its 59 references that are in the map. First posted on arXiv 21 Oct 2025.
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References · 26
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14 more
- 2022Randomly pivoted Cholesky: Practical approximation of a kernel matrix with few entry evaluationscited by 32
- 2023Feature Importance: A Closer Look at Shapley Values and LOCOcited by 23
- 2020Counterfactual Predictions under Runtime Confoundingcited by 22
- 2023One-Step Estimation of Differentiable Hilbert-Valued Parameterscited by 11
- 2023On Weighted Orthogonal Learners for Heterogeneous Treatment Effectscited by 10
- 2024Simplifying debiased inference via automatic differentiation and probabilistic programmingcited by 10
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- 2023Variable importance for causal forests: breaking down the heterogeneity of treatment effectscited by 6
- 2024Variable importance measures for heterogeneous treatment effects with survival outcomecited by 5
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- 2023An Approach to Nonparametric Inference on the Causal Dose Response Functioncited by 1
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Citing papers per year: 2026: 2.
2 papers.