I was also agreeing with the follow on paragraph: “ While predictive analytics are well developed, much less progress has been made on causal (attribution) analysis. Commercial predictive analytics tends to focus on what happened, or is predicted to happen (. click rates on web sites), with much less attention to why outcomes change in response to variations in inputs (. the wording or visual presentation of an on–line message). From the evaluation perspective, a limitation of predictive analysis is that it is not normally based on a theoretical framework, such as a theory of change, which explains the process through which outcomes are likely to be achieved. This is an area where there is great potential for collaboration between big data analytics and current impact evaluation methodologies ” My approach to connecting these two types of analysis is explained on the EvalC3 website . This involves connecting cross-case analysis (using predictive analytics tools, for example) to within-case analysis (using process tracing or simpler tools, for example) through carefully thought though case selection and comparison strategies.