Preference-based Conditional Treatment Effects and Policy Learning

This paper introduces a preference-based framework for conditional treatment effect estimation and policy learning, by defining the Conditional Preference-based Treatment Effect (CPTE). CPTE only requires that different outcomes can be compared, as opposed to traditional conditional average treatment effects (CATE) that quantify the conditional magnitude of an intervention on a real-valued outcome. This allows for flexible modeling of heterogeneous effects even when outcomes are multivariate, ordinal, or preference-driven. We show how CPTE enables interpretable estimands, and propose estimation strategies based on matching, quantile regression, and distributional methods. Building on this, we develop efficient influence function–based estimators to correct plug-in biases and optimize policy values. Through synthetic and semi-synthetic experiments, we demonstrate that our approach produces personalized policies that are robust to outcome heterogeneity and align with ground-truth preferences, improving decision quality over CATE-based baselines.