In this talk, I discuss how approaches that may seem very different (randomized controlled trials and Machine Learning) can in fact be complementary. RCT can serve as a useful benchmark to evaluate the real world performance of ML strategies to recover causal effects. ML methods can be used to investigate treatment effect heterogeneity, sort through a large number of possible treatments, etc. The talk concludes with a wish list for Machine learning specialists.