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Causal Inference and Explanation to Improve Human Health: 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.