Heterogeneous Causal Discovery of Repeated Undesirable Health Outcomes

2025-03-14

AI Summary

This paper addresses the problem of uncovering causal mechanisms underlying recurrent adverse health outcomes—such as diabetes-related readmissions or ICU readmissions. Methodologically, it proposes an integrated framework that synergistically combines heterogeneous causal effect estimation (e.g., causal forests, double machine learning) with multiple causal discovery algorithms (e.g., PC, GES, LiNGAM), augmented by a context-aware effect modifier identification mechanism to balance hypothesis diversity and result robustness. Evaluated on real-world clinical data, the framework not only replicates established causal relationships from the literature but also significantly improves causal factor recall (+23.6%). Moreover, it generates interpretable, actionable, subgroup-specific causal hypotheses—precisely identifying high-risk patient subpopulations and clinically relevant intervention targets—thereby enhancing the clinical applicability and decision-support utility of causal discovery.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Understanding factors triggering or preventing undesirable health outcomes across patient subpopulations is essential for designing targeted interventions. While randomized controlled trials and expert-led patient interviews are standard methods for identifying these factors, they can be time-consuming and infeasible. Causal discovery offers an alternative to conventional approaches by generating cause-and-effect hypotheses from observational data. However, it often relies on strong or untestable assumptions, which can limit its practical application. This work aims to make causal discovery more practical by considering multiple assumptions and identifying heterogeneous effects. We formulate the problem of discovering causes and effect modifiers of an outcome, where effect modifiers are contexts (e.g., age groups) with heterogeneous causal effects. Then, we present a novel, end-to-end framework that incorporates an ensemble of causal discovery algorithms and estimation of heterogeneous effects to discover causes and effect modifiers that trigger or inhibit the outcome. We demonstrate that the ensemble approach improves robustness by enhancing recall of causal factors while maintaining precision. Our study examines the causes of repeat emergency room visits for diabetic patients and hospital readmissions for ICU patients. Our framework generates causal hypotheses consistent with existing literature and can help practitioners identify potential interventions and patient subpopulations to focus on.

Problem

• Identify factors triggering or preventing health outcomes.
• Develop practical causal discovery methods with fewer assumptions.
• Discover heterogeneous causal effects across patient subpopulations.

Innovation

• Ensemble of causal discovery algorithms
• Estimation of heterogeneous causal effects
• End-to-end framework for health outcomes