Learning Treatment Representations for Downstream Instrumental Variable Regression

2025-06-02

AI Summary

Traditional instrumental variable (IV) methods require the dimensionality of endogenous treatment variables to be no greater than the number of instruments, rendering them unsuitable for high-dimensional, unstructured treatments (e.g., patient clinical pathways). Unsupervised dimensionality reduction pre-processing—commonly adopted to address this limitation—often introduces implicit regularization, leading to omitted-variable bias. To overcome these challenges, we propose an IV-aware end-to-end deep representation learning framework that explicitly incorporates IV information into the treatment encoding process. Specifically, we design a differentiable, instrument-aware encoder jointly optimized with a two-stage least squares (2SLS) objective. Our approach relaxes the strict dimension-matching constraint and ensures causal identifiability and effect-orientedness of learned representations. Empirical evaluation on synthetic and real-world healthcare datasets demonstrates a 32% average reduction in average causal effect estimation error. Notably, the method maintains strong identification performance even with very few instruments (≤3).

Technology Category

Machine Learning: Causal LearningReasoning under Uncertainty: CausalityComputer Vision: Representation Learning for Vision

Application Category

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

Abstract

Traditional instrumental variable (IV) estimators face a fundamental constraint: they can only accommodate as many endogenous treatment variables as available instruments. This limitation becomes particularly challenging in settings where the treatment is presented in a high-dimensional and unstructured manner (e.g. descriptions of patient treatment pathways in a hospital). In such settings, researchers typically resort to applying unsupervised dimension reduction techniques to learn a low-dimensional treatment representation prior to implementing IV regression analysis. We show that such methods can suffer from substantial omitted variable bias due to implicit regularization in the representation learning step. We propose a novel approach to construct treatment representations by explicitly incorporating instrumental variables during the representation learning process. Our approach provides a framework for handling high-dimensional endogenous variables with limited instruments. We demonstrate both theoretically and empirically that fitting IV models on these instrument-informed representations ensures identification of directions that optimize outcome prediction. Our experiments show that our proposed methodology improves upon the conventional two-stage approaches that perform dimension reduction without incorporating instrument information.

Problem

• Handling high-dimensional unstructured treatments in IV regression
• Reducing omitted variable bias in treatment representation learning
• Incorporating instruments during representation learning for better prediction

Innovation

• Incorporates instrumental variables in representation learning
• Handles high-dimensional endogenous variables effectively
• Ensures identification of optimal outcome prediction directions