A/B testing is very powerful. I like this type of experiment because it gives us the power to compare results and determine if something works better than another.
The A/B Test has a specific type that adds the time component, which is the Before and after A/B testing. In this test, the comparison is made between the situation of a given subject before and after an intervention.
Let's translate that sentence above into a real-world example.
A company wants to know if an advertisement would drive an increase in sales, so it can show that advertisement to a treatment group and compare the results with a control group that did not see the advertisement. The difference before and after the announcement would indicate whether the intervention was effective or not.
However, sometimes it is not possible to plan ahead and divide the control and treatment groups before the intervention.
That's when the Synthetic Control sample will come in handy. Using some statistics and machine learning, it is possible to simulate what would have happened to a sample if…
