DEBIAS (Durable Effects with Backdoor-Invariant Aggregated Symptoms)

DEBIAS is an algorithm designed to learn non-negative, clinically interpretable weights for outcome aggregation in psychiatric longitudinal data. It optimizes outcome definition to maximize causal identifiability of treatment effects while minimizing observed and latent confounding.