PerCaM-Health: Personalized Dynamic Causal Graphs for Healthcare Reasoning

TL;DR

PerCaM-Health introduces a personalized framework for learning dynamic causal graphs in healthcare, combining population knowledge with patient-specific data to improve causal inference and intervention predictions.

Contribution

It presents a novel method that adapts population-level causal graphs to individual patients over time, enabling personalized and interpretable causal reasoning in healthcare.

Findings

Improves graph recovery accuracy over baselines.

Enhances dynamic edge tracking in health data.

Increases intervention direction accuracy.

Abstract

Personalized healthcare decisions require reasoning about how physiological and behavioral variables influence an individual patient over time. Existing temporal causal discovery methods are poorly matched to this setting: cohort-level models provide stable but non-personalized structures, while per-patient discovery is unreliable because individual trajectories are short, noisy, irregular, and non-stationary. This creates a fundamental gap between population-level causal modeling and the patient-specific, time-varying mechanisms needed for intervention reasoning. We introduce PerCaM-Health, a framework for learning personalized dynamic causal graphs from longitudinal health data. The framework learns a knowledge-guided population temporal graph, then conservatively adapts and evolves it using patient-specific temporal evidence and rolling-window updates, producing interpretable and auditable graph sequences. By coupling these graphs with temporal structural equations, the framework enables patient-level counterfactual queries, such as estimating short-horizon outcome changes under hypothetical behavioral interventions. Experiments on a semi-synthetic dynamic health benchmark show that PerCaM-Health improves graph recovery, dynamic edge tracking, and intervention direction accuracy compared to cohort-level, per-patient, and non-personalized temporal baselines. These results demonstrate that jointly modeling personalization and temporal evolution yields more reliable causal structure and intervention reasoning.