Rank-Learner: Orthogonal Ranking of Treatment Effects

2026-02-03

AI Summary

This work proposes the first two-stage learning framework specifically designed for ranking individuals by treatment effect rather than estimating the conditional average treatment effect (CATE) precisely. By directly optimizing a pairwise ranking objective, the method recovers the true treatment effect ordering without explicitly modeling CATE. It incorporates Neyman orthogonality to ensure robustness against estimation errors in nuisance functions and remains compatible with arbitrary machine learning models, including neural networks. Empirical evaluations demonstrate that the proposed approach consistently outperforms conventional CATE estimators and non-orthogonal ranking methods across diverse settings, achieving both superior effectiveness and stability.

Technology Category

• Machine Learning: Learning Preferences or Rankings
• Reasoning under Uncertainty: Causality
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

Many decision-making problems require ranking individuals by their treatment effects rather than estimating the exact effect magnitudes. Examples include prioritizing patients for preventive care interventions, or ranking customers by the expected incremental impact of an advertisement. Surprisingly, while causal effect estimation has received substantial attention in the literature, the problem of directly learning rankings of treatment effects has largely remained unexplored. In this paper, we introduce Rank-Learner, a novel two-stage learner that directly learns the ranking of treatment effects from observational data. We first show that naive approaches based on precise treatment effect estimation solve a harder problem than necessary for ranking, while our Rank-Learner optimizes a pairwise learning objective that recovers the true treatment effect ordering, without explicit CATE estimation. We further show that our Rank-Learner is Neyman-orthogonal and thus comes with strong theoretical guarantees, including robustness to estimation errors in the nuisance functions. In addition, our Rank-Learner is model-agnostic, and can be instantiated with arbitrary machine learning models (e.g., neural networks). We demonstrate the effectiveness of our method through extensive experiments where Rank-Learner consistently outperforms standard CATE estimators and non-orthogonal ranking methods. Overall, we provide practitioners with a new, orthogonal two-stage learner for ranking individuals by their treatment effects.

Problem

• treatment effect ranking
• causal inference
• observational data
• individualized treatment effects
• ranking

Innovation

• Rank-Learner
• treatment effect ranking
• Neyman orthogonality
• causal inference
• model-agnostic