DeCaf: Decomposed Subspaces for Longitudinal Treatment Effect Estimation

Estimating how a patient’s outcomes might change under different treatment plans over time is a central need in personalized medicine. Longitudinal observational data, especially from electronic health records (EHR), can help address this by capturing how clinical decisions and patient states evolve across repeated visits. In real-world settings, however, treatment practices and data collection processes often vary across environments (e.g., across hospitals, cohorts or over time). As a result, signals that reflect the patient's underlying clinical state and treatment response, i.e., the factors through which interventions causally influence outcomes, can be entangled with environment-specific artifacts: systematic, setting-induced patterns (e.g., differences in treatment protocols and documentation practices), making multi-step counterfactual forecasting unreliable. To address this challenge, we propose DeCaf, a framework that learns a structured decomposed representation by separating a patient's longitudinal history into two latent subspaces: a outcome relevant subspace that encodes stable, intervention-relevant factors, and an environment/nuisance subspace that captures variation associated with treatment practice, cohort, and measurement processes. We then combine this representation with a trajectory generator to produce counterfactual trajectories under alternative treatment sequences, enabling coherent and stable forecasts over time.