Adaptive-CaRe: Adaptive Causal Regularization for Robust Outcome Prediction

2026-02-06

AI Summary

This work addresses the fragility of medical prediction models that often rely on spurious correlations, while purely causal approaches tend to sacrifice predictive accuracy. To reconcile this trade-off, the authors propose a model-agnostic adaptive causal regularization method that dynamically penalizes the discrepancy between a feature’s statistical predictive utility and its causal contribution through a tunable regularization term. By integrating causal structure learning with supervised learning, the method employs a hyperparameter λ to adaptively modulate the strength of causal constraints. Experimental results demonstrate that the approach consistently maintains high predictive performance across synthetic datasets, standard causal benchmarks, and real-world medical data, while significantly enhancing robustness against spurious associations.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior

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

Accurate prediction of outcomes is crucial for clinical decision-making and personalized patient care. Supervised machine learning algorithms, which are commonly used for outcome prediction in the medical domain, optimize for predictive accuracy, which can result in models latching onto spurious correlations instead of robust predictors. Causal structure learning methods on the other hand have the potential to provide robust predictors for the target, but can be too conservative because of algorithmic and data assumptions, resulting in loss of diagnostic precision. Therefore, we propose a novel model-agnostic regularization strategy, Adaptive-CaRe, for generalized outcome prediction in the medical domain. Adaptive-CaRe strikes a balance between both predictive value and causal robustness by incorporating a penalty that is proportional to the difference between the estimated statistical contribution and estimated causal contribution of the input features for model predictions. Our experiments on synthetic data establish the efficacy of the proposed Adaptive-CaRe regularizer in finding robust predictors for the target while maintaining competitive predictive accuracy. With experiments on a standard causal benchmark, we provide a blueprint for navigating the trade-off between predictive accuracy and causal robustness by tweaking the regularization strength, $\lambda$. Validation using real-world dataset confirms that the results translate to practical, real-domain settings. Therefore, Adaptive-CaRe provides a simple yet effective solution to the long-standing trade-off between predictive accuracy and causal robustness in the medical domain. Future work would involve studying alternate causal structure learning frameworks and complex classification models to provide deeper insights at a larger scale.

Problem

• outcome prediction
• spurious correlations
• causal robustness
• predictive accuracy
• medical domain

Innovation

• causal regularization
• model-agnostic
• robust prediction
• spurious correlations
• adaptive penalty