Inferring Causal Relationships from Large-Scale Time-Series

Dr. Samantha Kleinberg


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 complex causal relationships and their timing with minimal background knowledge and why inferring the effects of rare causes is both critically important and feasible.