Data Driven Causal Inference in Multiple Medical Systems


Fri Aug 16, 2013


Samantha Kleinberg
Stevens Institute of Technology


Data Sharing





One of the key problems we face with the accumulation of massive datasets is the transformation of data to actionable, causal, knowledge. Electronic health records enable us to conduct research on the same population that is being treated, but these data vary widely in quality, quantity, and structure. In order to know whether algorithms can accurately uncover new knowledge from these records, or whether findings can be extrapolated to new populations, they must be validated. One approach is to conduct the same study in multiple sites and compare results, but it is a challenge to determine whether differences are due to artifacts of the medical process, population differences, or failures of the methods used. This talk describes replicating experiments to infer causes of congestive heart failure from two medical systems and two patient populations.



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. Her research centers on developing methods for analyzing large-scale, complex, time-series data. In particular, her work develops methods for finding causes and automatically generating explanations for events, facilitating decision-making using massive datasets. She is the author of Causality, Probability, and Time (Cambridge University Press, 2012), and PI of an R01 from the National Library of Medicine.


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