Structure Maintained Representation Learning Neural Network for Causal Inference

2025-08-03

AI Summary

This paper addresses the challenges in individual treatment effect (ITE) estimation in high-dimensional settings: limited representation learning and adversarial network performance, coupled with the loss of structural dependencies between covariates and learned representations. To tackle these issues, we propose a structure-preserving representation learning framework. Its core innovation is a differentiable discriminator that explicitly enforces statistical dependence between the representation space and original covariates during adversarial training—thereby preserving information integrity while enhancing causal representation quality. We theoretically prove that our method minimizes an upper bound on ITE estimation error. Empirical evaluations on synthetic benchmarks and real-world electronic health records (MIMIC-III) demonstrate that our approach significantly outperforms state-of-the-art methods, particularly under small-sample and strong-confounding regimes, where it exhibits superior robustness.

Technology Category

• Machine Learning: Adversarial Learning & Robustness
• Multiagent Systems: Adversarial Agents
• Reasoning under Uncertainty: Causality

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• 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

Abstract

Recent developments in causal inference have greatly shifted the interest from estimating the average treatment effect to the individual treatment effect. In this article, we improve the predictive accuracy of representation learning and adversarial networks in estimating individual treatment effects by introducing a structure keeper which maintains the correlation between the baseline covariates and their corresponding representations in the high dimensional space. We train a discriminator at the end of representation layers to trade off representation balance and information loss. We show that the proposed discriminator minimizes an upper bound of the treatment estimation error. We can address the tradeoff between distribution balance and information loss by considering the correlations between the learned representation space and the original covariate feature space. We conduct extensive experiments with simulated and real-world observational data to show that our proposed Structure Maintained Representation Learning (SMRL) algorithm outperforms state-of-the-art methods. We also demonstrate the algorithms on real electronic health record data from the MIMIC-III database.

Problem

• Improves individual treatment effect estimation accuracy
• Maintains covariate-representation correlation in high dimensions
• Balances representation and minimizes treatment estimation error

Innovation

• Structure keeper maintains covariate-representation correlation
• Discriminator balances representation and minimizes error
• SMRL algorithm outperforms in simulated and real data