Econometric advances in causal inference: The machine learning revolution

This is one of the challenges that new and fast-growing econometric literature is beginning to tackle in addressing causal inference problems with machine learning methods. Yet, empirical economics still has not really made use of the strengths of these modern approaches. Here, we revisit groundbreaking empirical work through the perspective of causal machine learning methods to connect econometric theory with applied economics. In particular, we will cover double machine learning, causal forests, and more general machine learning methodologies, both in the setting of average treatment effects and heterogeneous treatment effects. We demonstrate the application of these methods in diverse settings and discuss their significance and additional benefits relative to classical approaches that were utilized in the original studies.