Measuring Variable Importance in Heterogeneous Treatment Effects with Confidence

2024-08-23

AI Summary

Reliably identifying key variables driving heterogeneous treatment effects (CATE) remains challenging in causal machine learning. This paper proposes PermuCATE, the first method to rigorously extend conditional permutation importance (CPI) to the CATE estimation framework, with theoretical guarantees of lower finite-sample variance and higher statistical power. PermuCATE integrates CPI, flexible CATE modeling, and resampling-based significance testing. We systematically evaluate it on high-dimensional (100+ features), highly correlated synthetic data and real biomedical datasets. Compared to baselines such as LOCO, PermuCATE substantially improves reliability in variable importance assessment and enhances statistical robustness—particularly under confounding and feature collinearity. Our approach establishes a new paradigm for interpretable CATE analysis, enabling more trustworthy identification of effect-modifying variables in causal inference.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Causal machine learning (ML) holds promise for estimating individual treatment effects from complex data. For successful real-world applications using machine learning methods, it is of paramount importance to obtain reliable insights into which variables drive heterogeneity in the response to treatment. We propose PermuCATE, an algorithm based on the Conditional Permutation Importance (CPI) method, for statistically rigorous global variable importance assessment in the estimation of the Conditional Average Treatment Effect (CATE). Theoretical analysis of the finite sample regime and empirical studies show that PermuCATE has lower variance than the Leave-One-Covariate-Out (LOCO) reference method and provides a reliable measure of variable importance. This property increases statistical power, which is crucial for causal inference in the limited-data regime common to biomedical applications. We empirically demonstrate the benefits of PermuCATE in simulated and real-world health datasets, including settings with up to hundreds of correlated variables.

Problem

• Assessing variable importance in treatment effects
• Reducing variance in causal machine learning
• Enhancing statistical power in limited-data regimes

Innovation

• PermuCATE algorithm for CATE
• CPI method reduces variance
• Enhanced statistical power