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Characterizing database granularity using SNOMED-CT hierarchy., , , , , , , , , and 9 other author(s). AMIA, AMIA, (2020)Phenotype Concept Set Construction from Concept Pair Likelihoods., , , , , , , , , and . AMIA, AMIA, (2020)The Medical Deconfounder: Assessing Treatment Effects with Electronic Health Records., , , , , and . MLHC, volume 106 of Proceedings of Machine Learning Research, page 490-512. PMLR, (2019)What are the Desired Characteristics of Calibration Sets? Identifying Correlates on Long Form Scientific Summarization., , , , , , , , , and . ACL (1), page 10520-10542. Association for Computational Linguistics, (2023)Evaluation of Large-scale Propensity Score Modeling and Covariate Balance on Potential Unmeasured Confounding in Observational Research., , , , , , and . AMIA, AMIA, (2020)Adapting electronic health records-derived phenotypes to claims data: Lessons learned in using limited clinical data for phenotyping., , , , , and . J. Biomed. Informatics, (2020)The Multi-Outcome Medical Deconfounder: Assessing Treatment Effect on Multiple Renal Measures., , , , , and . AMIA, AMIA, (2020)PHenotype Observed Entity Baseline Endorsements (PHOEBE) - recommender system for concept selection in phenotype algorithm development., , and . AMIA, AMIA, (2021)Causal fairness assessment of treatment allocation with electronic health records., , , , , , and . J. Biomed. Informatics, (2024)Ontologizing Health Systems Data at Scale: Making Translational Discovery a Reality., , , , , , , , , and 21 other author(s). CoRR, (2022)