  A new and rapidly growing econometric literature is making advances in the
problem of using machine learning methods for causal inference questions. Yet,
the empirical economics literature has not started to fully exploit the
strengths of these modern methods. We revisit influential empirical studies
with causal machine learning methods and identify several advantages of using
these techniques. We show that these advantages and their implications are
empirically relevant and that the use of these methods can improve the
credibility of causal analysis.
