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Please note that all times are shown in the time zone of the conference. The current conference time is: 24th Aug 2026, 05:31:25am America, Santiago
Paulo Quinsacara, Efraín Campusano, Pablo Schwarzenberg, Hernan Astudillo, Carla Taramasco, Billy peralta
Universidad Andrés Bello - (CL), Chile
The COVID-19 pandemic has been one of the most severe public health crises of recent decades, exerting profound impacts on population health, economic activity, and social dynamics. In Chile, systematic epidemiological surveillance conducted by the Ministry of Health (MINSAL) during 2020--2021 yielded an anonymized dataset of 54 variables and 6,764,619 patient records. This study aims to infer an interpretable causal structure that relates demographic, clinical, and health-system-related variables to COVID-19 outcomes using observational surveillance data. We learn a directed acyclic graph (DAG) with Greedy Equivalence Search (GES) and evaluate its plausibility using a permutation-based falsification test in DoWhy, which quantifies violations of the Local Markov Condition (LMC) relative to randomized baselines. The learned GES graph is informative under falsification (0/20 permutations in the Markov equivalence class; p-value = 0.00) and exhibits fewer LMC violations than permuted graphs (79/1629), indicating that it encodes characteristic conditional independencies unlikely to arise by chance. Overall, the resulting structure provides a compact and interpretable causal hypothesis space to support domain interpretation and to prioritize variables and pathways for subsequent causal effect analyses.
