Causal discovery and inference in COVID-19: A multiscale longitudinal analysis using mixtures of DAGs

Real-world longitudinal clinical datasets present major challenges for causal discovery due to mixed variable types, temporal dependencies, treatment-selection mechanisms, missingness, and patient heterogeneity. We extended the Causal Inference over Mixtures (CIM) framework to accommodate mixed-type longitudinal data and applied it to 1,937 hospitalized patients from the Khorshid COVID Cohort. The analysis integrated admission characteristics, symptom trajectories, in-hospital treatments, laboratory measures, and short-term outcomes up to 12 weeks after discharge using mixed conditional-independence testing, bootstrap stability selection, graph-based interpretation, and follow-up outcome assessment. The recovered ancestral structures revealed clinically coherent symptom clusters at admission and persistent associations among several post-discharge symptoms, including cough, fatigue/weakness, shortness of breath, weight loss, and loss of smell. Treatment-related associations were interpreted cautiously because cumulative treatment exposure may reflect disease severity, hospitalization duration, physician decision-making, and other selection mechanisms. In particular, the observed associations involving ceftriaxone and interferon-