Easy Methods for Causal Inference | by Dr. Robert Kübler | Feb, 2024

Use your favorite models in combination with meta-learners to make valid causal statements

Imagine that you have built an awesome machine learning model that can forecast your target value with great accuracy. In some cases, your job might be over at this point. However, often the business does not only want to know what will happen but how to influence the outcome as well. True to the motto:

Knowing the future is silver, being able to change it is golden.

This simple truth goes without saying, you know it from your personal life. Knowing the lottery numbers for the next week is good, but only if you can adjust your numbers accordingly.

As a business example, take the problem of customer churn, i.e., customers who stop doing business with you. Knowing that a customer wants to leave you is good, but the real question is: how to prevent this customer from churning?

The business wants some way of intervention for example by giving out a coupon or granting this customer some kind of membership upgrade. Something that the business can influence to decrease the probability of churn.

If x = “give the customer a coupon” and y = churn probability, we want to be able to make causal statements: If I do x, what happens with y? People also like to call it what-if scenarios. x is called a treatment variable.

This is more difficult than making correlational statements. Observing that ice cream sales correlate with shark attacks is easy. But does one cause the other? Probably not. It is rather the good weather that drives people to buy ice cream and take a swim in the sea, exposing them to sharks. So, closing all ice cream parlors as a way to decrease shark attacks will most likely not work.

In this article, I will show you how to reach the correct conclusions. The methods I am going to show you are extremely simple to carry out on an algorithmic level: it just involves training models and post-processing their predictions slightly…