One of the key problems we face with the accumulation of massive datasets (such as electronic health records and stock market data) is the transformation of data to actionable knowledge. In order to use the information gained from analyzing these data to intervene to, say, treat patients or create new fiscal policies, we need to know that the relationships we have inferred are causal. Further, we need to know the time over which the relationship takes place in order to know when to intervene. In this talk I discuss recent methods for finding causal relationships and their timing from uncertain data with minimal background knowledge and their applications to observational health data.
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.