Unveiling the Potential of Robustness in Evaluating Causal Inference Models

arXiv:2402.18392v1 Announce Type: new
Abstract: The growing demand for personalized decision-making has led to a surge of interest in estimating the Conditional Average Treatment Effect (CATE). The intersection of machine learning and causal inference has yielded various effective CATE estimators. However, deploying these estimators in practice is often hindered by the absence of counterfactual labels, making it challenging to select the desirable CATE estimator using conventional model selection procedures like cross-validation. Existing approaches for CATE estimator selection, such as plug-in …

abstract arxiv causal inference cs.ai cs.lg decision demand econ.em inference intersection machine machine learning making personalized practice robustness stat.ml treatment type