arXiv Machine Learning
Jul 21

MinShap: A Shapley-Based Framework for Feature Redundancy

arXiv:2604. 15107v2 Announce Type: replace-cross Abstract: Shapley values provide a flexible framework for attributing feature contributions to model predictions, but they are not naturally suited for feature selection: a feature may receive a positive attribution even when it is redundant given the remaining variables.

By Chenghui Zheng, Garvesh Raskutti
arXiv Statistics ML
Aug 25

Model-Agnostic Covariate-Assisted Inference on Partially Identified Causal Effects

The paper introduces a model‑agnostic inference framework for partially identified causal effects that leverages covariate information without requiring discrete covariates or accurate conditional distribution estimates. Using duality theory for optimal transport, the method delivers uniformly valid inference in randomized experiments, is doubly robust in observational settings, achieves asymptotic unbiasedness when nuisance parameters converge semiparametrically, and allows multiplier‑bootstrap selection of covariates and models while remaining computationally efficient. Empirical applications show the approach consistently narrows identified sets and confidence intervals without imposing extra structural assumptions.

By Wenlong Ji, Lihua Lei, Asher Spector