Causal Rule Discovery with User-Guided Embeddings for Explainable Clinical Outcome Prediction
Keywords:
causal rule discovery, user-guided embeddings, clinical outcome prediction, explainable AI, healthcare decision support, interpretability, causal inference, human-in-the-loopAbstract
Clinical outcome prediction is increasingly reliant on complex machine learning models that sacrifice transparency for predictive performance, creating tensions in high-stakes medical decision-making. Causal rule discovery offers a pathway to reconcile accuracy with interpretability by generating if-then decision rules grounded in causal relationships. However, constructing causally informative rules from high-dimensional, heterogeneous clinical data remains challenging. This paper proposes a system that integrates user-guided embedding techniques with causal structure learning to produce explainable outcome predictions. The architecture enables clinicians to steer the latent feature representation through iterative relevance feedback, embedding domain knowledge directly into the representation space. Causal discovery algorithms then extract directed rules from this refined space, yielding a transparent rule set that supports both prediction and clinical reasoning. We examine the structural trade-offs of this hybrid system, focusing on the interplay between embedding flexibility, causal identifiability, predictive power, and explainability. Through a system-level analysis, we discuss deployment considerations, including data governance, fairness audits, robustness to distributional shifts, and regulatory compliance. The framework underscores how human-in-the-loop representation learning can serve as a bridge between black-box modeling and formal causal inference, enabling sustainable, auditable, and clinically actionable decision support.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
