Product Analytics: Applied Data Science Techniques for Actionable Consumer Insights

11. In Pursuit of the Experiment, Continued

In Chapter 10, we discussed the nuts and bolts of causal inference from observational data. We explored examples of natural experiments, where assignment into treatment and control groups occurs due to some natural process. We also went through an application of the difference-in-difference design. A difference-in-difference (DID) design can be used to model both natural experiments and quasi-experiments, which have a counterfactual, as in the designated market area (DMA) example.

Recall that a quasi-experimental design is used when the assignment process is not random. The reality is the vast majority of cases of causal inference from observational data are quasi-experiments, not natural experiments. ...