Learn how to build neural networks for direct causal inference
Building machine learning models is fairly easy nowadays, but often, making good predictions is not enough. On top, we want to make causal statements about interventions. Knowing with high accuracy that a customer will leave our company is good, but knowing what to do about it — for example sending a coupon — is much better. This is a bit more involved, and I explained the basics in my other article.
I recommend reading this article before you continue. I showed you how you can easily come to causal statements whenever your features form a sufficient adjustment set, which I will also assume for the rest of the article.
The estimation works using so-called meta-learners. Among them, there are the S- and the T-learners, each with their own set of disadvantages. In this article, I will show you another approach that can be seen as a tradeoff between these two meta-learners that can give you better results.
Let us assume that you have a dataset (X, t, y), where X denotes some features, t is a distinct binary treatment, and y is the outcome. Let us briefly recap how the S- and T-learners work and when they don’t perform well.
S-learner
If you use an S-learner, you fix a model M and train it on the dataset such that M(X, t) ≈ y. Then, you compute
Treatment Effects = M(X, 1) – M(X, 0)
and that’s it.
The problem with this approach is that the mode could choose to ignore the feature t completely. This typically happens if you already have hundreds of features in X, and t drowns in this noise. If this…
