The difference-in-difference (DiD) is one of the most popular approaches to evaluate causal effects of programs or policies. The idea is very simple: a treatment group is affected by an external change in one period, and the main aim is to evaluate how this treated group changes after the policy, regarding a control group that is not affected. So it controls the double difference (changes over time and over control group). Although some assumptions have to be assumed, its flexibility and requiring a relatively small volume of data yield to a large number of papers and documents that use it on topics as competition policy, merger evaluations, political economy, and so on.