Title: A Relative Error-Based Evaluation Framework of Heterogeneous Treatment Effect Estimators
Primary area: causal reasoning
Keywords: Causal Inference, Conditional Average Treatment Effect, Relative Error, Robust Evaluation

Abstract: While significant progress has been made in heterogeneous treatment effect (HTE) estimation, the evaluation of HTE estimators remains underdeveloped. In this article, we propose a robust evaluation framework based on relative error, which quantifies performance differences between two HTE estimators. We first derive the key theoretical conditions on the nuisance parameters that are necessary to  achieve a robust estimator of relative error. Building on these conditions, we introduce novel loss functions and design a neural network architecture to estimate nuisance parameters, thereby obtaining a robust estimation of relative error. We provide the large sample properties of the proposed relative error estimator. Furthermore, beyond evaluation,  we propose a new learning algorithm for HTE that leverages both the previously HTE estimators and the nuisance parameters learned through our neural network architecture. Extensive experiments demonstrate that our evaluation framework supports reliable comparisons across HTE estimators, and the proposed learning algorithm for HTE exhibits desirable performance.

TL;DR: We propose a robust relative error-based evaluation framework for heterogeneous treatment effect estimators.