Orthogonal Representation Learning for Estimating Causal Quantities

2025-02-06

AI Summary

Existing causal representation learning methods enable end-to-end estimation but lack the double robustness and near-oracle efficiency of Neyman-orthogonal learners, and their reliance on explicit balancing constraints often leads to inconsistent estimation. Method: We propose Orthogonal Representation Learning (OR-learners), the first framework unifying Neyman orthogonality with representation learning at the latent feature level—enabling consistent, doubly robust, and semiparametrically efficient estimation of causal quantities (e.g., CATE) under arbitrary representations, without requiring explicit balancing constraints. Contribution/Results: We establish theoretical guarantees of consistency and semiparametric efficiency. Empirically, OR-learners significantly outperform state-of-the-art methods across multiple benchmarks, achieving the current best performance in causal inference.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Computer Vision: Representation Learning for Vision

Application Category

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

Abstract

Representation learning is widely used for estimating causal quantities (e.g., the conditional average treatment effect) from observational data. While existing representation learning methods have the benefit of allowing for end-to-end learning, they do not have favorable theoretical properties of Neyman-orthogonal learners, such as double robustness and quasi-oracle efficiency. Also, such representation learning methods often employ additional constraints, like balancing, which may even lead to inconsistent estimation. In this paper, we propose a novel class of Neyman-orthogonal learners for causal quantities defined at the representation level, which we call OR-learners. Our OR-learners have several practical advantages: they allow for consistent estimation of causal quantities based on any learned representation, while offering favorable theoretical properties including double robustness and quasi-oracle efficiency. In multiple experiments, we show that, under certain regularity conditions, our OR-learners improve existing representation learning methods and achieve state-of-the-art performance. To the best of our knowledge, our OR-learners are the first work to offer a unified framework of representation learning methods and Neyman-orthogonal learners for causal quantities estimation.

Problem

• Estimating causal quantities from observational data
• Improving representation learning with Neyman-orthogonal properties
• Providing consistent and efficient causal estimation framework

Innovation

• Neyman-orthogonal learners
• double robustness
• quasi-oracle efficiency