Unveiling the Potential of Robustness in Selecting Conditional Average Treatment Effect Estimators
This work addresses a specific challenge in causal inference for personalized decision-making, offering an incremental improvement over existing selection methods.
The paper tackles the problem of selecting robust Conditional Average Treatment Effect (CATE) estimators in observational data by introducing a Distributionally Robust Metric (DRM) that is nuisance-free and prioritizes robustness to distribution shifts, with experimental validation showing effectiveness against covariate shift and hidden confounders.
The growing demand for personalized decision-making has led to a surge of interest in estimating the Conditional Average Treatment Effect (CATE). Various types of CATE estimators have been developed with advancements in machine learning and causal inference. However, selecting the desirable CATE estimator through a conventional model validation procedure remains impractical due to the absence of counterfactual outcomes in observational data. Existing approaches for CATE estimator selection, such as plug-in and pseudo-outcome metrics, face two challenges. First, they must determine the metric form and the underlying machine learning models for fitting nuisance parameters (e.g., outcome function, propensity function, and plug-in learner). Second, they lack a specific focus on selecting a robust CATE estimator. To address these challenges, this paper introduces a Distributionally Robust Metric (DRM) for CATE estimator selection. The proposed DRM is nuisance-free, eliminating the need to fit models for nuisance parameters, and it effectively prioritizes the selection of a distributionally robust CATE estimator. The experimental results validate the effectiveness of the DRM method in selecting CATE estimators that are robust to the distribution shift incurred by covariate shift and hidden confounders.