PROJECT SUMMARY The long-term goal of our parent project, “Discovering and applying knowledge in clinical databases,” is to learn from data in the electronic health record (EHR) and to apply that knowledge to understand and improve health. Its first two aims are as follows: (1) Taking a knowledge engineering approach, study the effect of preprocessing and analytic choices on reducing health care process bias, and using machine learning techniques, learn more about health care process bias. (2) Taking a more empirical approach, use dynamic latent factor modeling and variation inference to accommodate health care process bias, learning how a patient's health state and health processes affect censoring, exploiting information from many variables at once. For this supplement, we plan to focus on COVID-19. The emergence of the virus SARS-CoV-2 and its corresponding disease, COVID-19, has led to about 100,000 deaths in the US and great economic loss and human suffering. Carrying out randomized clinical trials to assess treatment is essential but stymied by the difficulty recruiting sufficient patients and the urgency of the question. Clinical databases are beginning to fill with COVID-19 patients, but the acuity and severity of the disease make drawing causal conclusion much more difficult, resulting in a literature filled with conflicting observational studies. We propose to employ structural causal modeling in the study of COVID-19, engaging expertise in such modeling. We will use the Columbia University Irving Medical Center's clinical data warehouse with over 6000 testing positive for SARS-CoV-2 and the Observational Health Data Science and Informatics (OHDSI) network, which includes most COVID-19 patients in Korea, Spain, the US Veterans Administration, Stanford, Tufts, and new sites coming on board.