We also expect tremendous development to come from bridging the gap between the qualitative accounts of mechanisms and mechanistic explanation developed in the new mechanism and quantitative theories of discovery from the discipline of machine learning and causal modeling (Spirtes et al. 2000; Pearl 2009). The latter offer tools to mine correlational data for causal dependencies. Such tools might escape more qualitative, historical approaches and might, in fact, go beyond the common strategies that scientists traditionally use. Such tools also offer a means to assess discovery strategies by exploring the conditions under which they succeed and fail and the efficiency with which they deliver verdicts on causal hypotheses.