Causal Machine Learning and its use for public policy

In recent years, microeconometrics experienced the "credibility revolution", culminating in the 2021 Nobel prices for David Card, Josh Angrist, and Guido Imbens. This "revolution" in how to do empirical work led to more reliable empirical knowledge of the causal effects of certain public policies. In parallel, computer science, and to some extent also statistics, developed powerful (so-called Machine Learning) algorithms that are very successful in prediction tasks. The new literature on Causal Machine Learning unites these developments by using algorithms originating in Machine Learning for improved causal analysis. In this non-technical overview, I review some of these approaches. Subsequently, I use an empirical example from the field of active labour market programme evaluation to showcase how Causal Machine Learning can be applied to improve the usefulness of such studies. I conclude with some considerations about shortcomings and possible future developments of these methods as well as wider implications for teaching and empirical studies.