Preference-based Conditional Treatment Effects and Policy Learning

2026-02-03

AI Summary

This study addresses the limitation of traditional conditional treatment effect estimation, which is confined to scalar outcomes and struggles with multidimensional, ordinal, or merely rankable preference-based outcomes. The authors propose a Preference-based Conditional Treatment Effect (CPTE) framework that characterizes heterogeneous treatment effects by modeling the ordinal relationships among outcomes and enables policy learning. This framework unifies various preference-driven causal estimands for the first time, establishes novel identifiability conditions with clear interpretability, and integrates matching, quantile regression, and distributional regression to construct an efficient influence-function-based estimator. This estimator corrects plug-in bias and enhances policy value. Experiments on synthetic and semi-synthetic data demonstrate that the proposed method significantly outperforms existing approaches, confirming its effectiveness and practical potential.

Technology Category

• Machine Learning: Learning Preferences or Rankings
• Knowledge Representation and Reasoning: Preferences
• Reasoning under Uncertainty: Causality

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments

Abstract

We introduce a new preference-based framework for conditional treatment effect estimation and policy learning, built on the Conditional Preference-based Treatment Effect (CPTE). CPTE requires only that outcomes be ranked under a preference rule, unlocking flexible modeling of heterogeneous effects with multivariate, ordinal, or preference-driven outcomes. This unifies applications such as conditional probability of necessity and sufficiency, conditional Win Ratio, and Generalized Pairwise Comparisons. Despite the intrinsic non-identifiability of comparison-based estimands, CPTE provides interpretable targets and delivers new identifiability conditions for previous unidentifiable estimands. We present estimation strategies via matching, quantile, and distributional regression, and further design efficient influence-function estimators to correct plug-in bias and maximize policy value. Synthetic and semi-synthetic experiments demonstrate clear performance gains and practical impact.

Problem

• Conditional Treatment Effect
• Preference-based Learning
• Policy Learning
• Heterogeneous Treatment Effects
• Non-identifiability

Innovation

• Conditional Preference-based Treatment Effect
• Policy Learning
• Heterogeneous Treatment Effects
• Identifiability
• Influence Function Estimation