One of the key problems we face with the accumulation of massive datasets (such as from electronic health records, financial markets, and social networks) 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, and what other factors are needed for the cause to be effective in order to intervene. This talk will discuss the challenges inherent in inference from observational data, recent work addressing these, and the current limits of causal inference.