Causal inference using synthetic controls

Last year, Xandr became part of Microsoft, whose mission it is “to empower every person and every organization on the planet to achieve more.” In order to do that, we need to understand what makes every person and organization successful in the first place. We know that every change we make (to our products or algorithms) and any change our customers make (to their setups or campaigns) can have an effect, be it positive or negative. This is where causal inference comes into play. In this article, I describe how we approach some of these problems using Machine Learning. Unfortunately, conventional Machine Learning methods typically suffer from a critical shortcoming when it comes to causal inference: They are designed to exploit correlations rather than causal relationships. This is often good enough for making predictions, but it is insufficient when it comes to understanding causes and effects (i.e., the why or what if). This is simply because correlation does not imply causation. <a href="https://medium.com/data-science-at-microsoft/causal-inference-using-synthetic-controls-d96a890c83a7">Website</a>