A review of causal inference for biomedical informatics

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.

Figure optionsDownload as PowerPoint slideHighlights
►  Explanation of why causality is critical to prediction, explanation, and policy.
►  Brief history of philosophical and epidemiological methods for causal inference.
►  Review of methods for inference from observational data.
►  Covers graphical models, Granger causality, and methods based on temporal logic.