title: A review of causal inference for biomedical informatics.
abstract: Causality is an important concept throughout the health sciences and is particularly vital for informatics work such as finding adverse drug events or risk factors for disease using electronic health records. While philosophers and scientists working for centuries on formalizing what makes something a cause have not reached a consensus, new methods for inference show that we can make progress in this area in many practical cases. This article reviews core concepts in understanding and identifying causality and then reviews current computational methods for inference and explanation, focusing on inference from large-scale observational data. While the problem is not fully solved, we show that graphical models and Granger causality provide useful frameworks for inference and that a more recent approach based on temporal logic addresses some of the limitations of these methods.
authors.affiliation: Department of Biomedical Informatics, Columbia University, New York, NY 10032, United States. samantha@dbmi.columbia.edu
authors.forename: Samantha
authors.initials: S
authors.lastname: Kleinberg
authors.forename: George
authors.initials: G
authors.lastname: Hripcsak
mesh_terms: D001499:Bayes Theorem; D015984:Causality; D064420:Drug-Related Side Effects and Adverse Reactions; D057286:Electronic Health Records; D004813:Epidemiology; D006801:Humans; D008490:Medical Informatics; D057285:Precision Medicine; D012307:Risk Factors