  We discuss the relevance of the recent Machine Learning (ML) literature for
economics and econometrics. First we discuss the differences in goals, methods
and settings between the ML literature and the traditional econometrics and
statistics literatures. Then we discuss some specific methods from the machine
learning literature that we view as important for empirical researchers in
economics. These include supervised learning methods for regression and
classification, unsupervised learning methods, as well as matrix completion
methods. Finally, we highlight newly developed methods at the intersection of
ML and econometrics, methods that typically perform better than either
off-the-shelf ML or more traditional econometric methods when applied to
particular classes of problems, problems that include causal inference for
average treatment effects, optimal policy estimation, and estimation of the
counterfactual effect of price changes in consumer choice models.
