Let us suppose that one has chosen one's case (or cases) according to one of the techniques (or some combination of techniques) described in the previous chapter. And let us further suppose that one has refined one's research question into a specific (X1/Y) hypothesis. One then faces a problem of internal validity. How does one construct a research design that might illuminate the causal relationship of interest?

The fundamental problem of causal inference is that one cannot rerun history to see what effects X1 actually had on Y in a particular case. At an ontological level, this problem is unsolvable. There are no time machines. However, there are various ways of reducing uncertainty so that causal inference is possible, and indeed quite plausible. The argument of this chapter is that the various methods of doing so are all quasi-experimental in nature. This is because the true experiment is the closest approximation we have at our disposal to a time machine. Through this technique, and others modeled on it, one can imagine what it would be like to go back in time, alter a “treatment,” and observe its true causal effect.

This chapter thus calls into question some of the usual assumptions applied to case study research. Most case study researchers perceive only a distant and tenuous connection between their work and the laboratory experiment, with a manipulated treatment and randomized control.