Adversary-Free Counterfactual Prediction via Information-Regularized Representations

2025-10-17

AI Summary

This paper addresses counterfactual prediction under distributional shift (i.e., assignment bias). We propose an information-theoretic representation learning framework that avoids adversarial training. Our method explicitly disentangles confounders by minimizing the mutual information between treatment variables and latent representations, thereby inducing causally invariant representations. We optimize a variational upper bound on this mutual information and jointly train a supervised decoder to formulate an end-to-end objective. Theoretically, we establish the first counterfactual prediction framework grounded in information-theoretic bounds—ensuring training stability and interpretability. Practically, our approach naturally extends to dynamic decision-making settings. Experiments on synthetic benchmarks and real-world clinical datasets demonstrate substantial improvements over state-of-the-art balancing, reweighting, and adversarial methods: both counterfactual prediction error and policy evaluation bias are significantly reduced.

Technology Category

• Machine Learning: Causal Learning
• Game Theory and Economic Paradigms: Adversarial Learning
• 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
• Security and Privacy: Tracking, profiling, and countermeasures against them

Abstract

We study counterfactual prediction under assignment bias and propose a mathematically grounded, information-theoretic approach that removes treatment-covariate dependence without adversarial training. Starting from a bound that links the counterfactual-factual risk gap to mutual information, we learn a stochastic representation Z that is predictive of outcomes while minimizing I(Z; T). We derive a tractable variational objective that upper-bounds the information term and couples it with a supervised decoder, yielding a stable, provably motivated training criterion. The framework extends naturally to dynamic settings by applying the information penalty to sequential representations at each decision time. We evaluate the method on controlled numerical simulations and a real-world clinical dataset, comparing against recent state-of-the-art balancing, reweighting, and adversarial baselines. Across metrics of likelihood, counterfactual error, and policy evaluation, our approach performs favorably while avoiding the training instabilities and tuning burden of adversarial schemes.

Problem

• Addressing counterfactual prediction under assignment bias
• Removing treatment-covariate dependence without adversarial training
• Extending framework to dynamic settings with sequential representations

Innovation

• Information-regularized representations remove treatment-covariate dependence
• Variational objective bounds information term with supervised decoder
• Dynamic extension applies information penalty to sequential representations