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Causal Inference And Explanation To Improve Human Health

Abstract

Massive amounts of medical data such as from electronic health records and body-worn sensors are being collected and mined by researchers, but translating findings into actionable knowledge remains difficult. The first challenge is finding causes, rather than correlations, when the data are highly noisy and often missing. The second is using these to explain specific cases, such as why an individual’s blood glucose is raised. In this talk I discuss new methods for both causal inference and explanation, and show how these could be used to provide individualized feedback to patients. In the second part of this talk I discuss our recent work using multiple sensing modalities to automatically identify eating and how this can ultimately be used to support individuals with chronic disease.

Bio

Samantha Kleinberg is an Assistant Professor of Computer Science at Stevens Institute of Technology. She received her PhD in Computer Science from New York University in 2010 and was a Computing Innovation Fellow at Columbia University in the Department of Biomedical informatics from 2010-2012. She is the recipient of NSF CAREER and JSMF Complex Systems Scholar Awards and her work is also supported by the NIH through an R01. She is the author of Causality, Probability, and Time (Cambridge University Press, 2012) and Why: A Guide to Finding and Using Causes (O’Reilly Media, 2015).