Causal Inference in Python

Preface

Picture yourself as a new data scientist who’s just starting out in a fast-growing and promising startup. Although you haven’t mastered machine learning, you feel pretty confident about your skills. You’ve completed dozens of online courses on the subject and even gotten a few good ranks in prediction competitions. You are now ready to apply all that knowledge to the real world and you can’t wait for it. Life is good.

Then, your team leader comes with a graph that looks something like this:

And an accompanying question: “Hey, we want you to figure out how many additional customers paid marketing is really bringing us. When we turned it on, we definitely saw some customers coming from the paid marketing channel, but it looks like we also had a drop in organic applications. We think some of the customers from paid marketing would have come to us even without paid marketing.” Well…you were expecting a challenge, but this?! How could you know what would have happened without paid marketing? I guess you could compare the total number of applications, organic and paid, before and after turning on the marketing campaign. But in a fast growing and dynamic company, how would you know that nothing else changes when they launch the campaign (see Figure P-1)?

Figure P-1. Fast-growing ...