Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE

2025-02-07

AI Summary

This paper addresses the identifiability challenge of the conditional average treatment effect (CATE) in the presence of post-treatment confounding covariates. To mitigate their spurious influence, we propose causal representation learning. We first systematically reveal the counterfactual supervisory role of simulators in CATE estimation and accordingly design SimPONet: a method grounded in a theoretically derived generalization bound to formulate its loss function, augmented with a correlation-aware adaptive mechanism that dynamically modulates simulator contributions. SimPONet integrates causal representation learning, contrastive learning, and simulator-enhanced training to explicitly model distribution shifts. Extensive experiments across diverse distribution shift settings demonstrate that SimPONet consistently outperforms state-of-the-art methods, validating both the efficacy and robustness of simulator-driven causal representation learning for CATE estimation.

Technology Category

• Machine Learning: Causal Learning
• Search and Optimization: Sampling/Simulation-based Search
• Reasoning under Uncertainty: Causality

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Treatment effect estimation involves assessing the impact of different treatments on individual outcomes. Current methods estimate Conditional Average Treatment Effect (CATE) using observational datasets where covariates are collected before treatment assignment and outcomes are observed afterward, under assumptions like positivity and unconfoundedness. In this paper, we address a scenario where both covariates and outcomes are gathered after treatment. We show that post-treatment covariates render CATE unidentifiable, and recovering CATE requires learning treatment-independent causal representations. Prior work shows that such representations can be learned through contrastive learning if counterfactual supervision is available in observational data. However, since counterfactuals are rare, other works have explored using simulators that offer synthetic counterfactual supervision. Our goal in this paper is to systematically analyze the role of simulators in estimating CATE. We analyze the CATE error of several baselines and highlight their limitations. We then establish a generalization bound that characterizes the CATE error from jointly training on real and simulated distributions, as a function of the real-simulator mismatch. Finally, we introduce SimPONet, a novel method whose loss function is inspired from our generalization bound. We further show how SimPONet adjusts the simulator's influence on the learning objective based on the simulator's relevance to the CATE task. We experiment with various DGPs, by systematically varying the real-simulator distribution gap to evaluate SimPONet's efficacy against state-of-the-art CATE baselines.

Problem

• Estimate CATE using post-treatment covariates
• Learn causal representations from simulators
• Analyze simulator impact on CATE estimation

Innovation

• Simulator for causal learning
• SimPONet adjusts simulator influence
• Generalization bound for CATE