Using Machine Learning in Economics - DATAVERSITY

Jon Levin, a professor of economics at Stanford, recently wrote in Forbes, "Machine learning methods are really powerful for fitting predictive models and for doing classification on large-scale, high-dimensional data. These are the data we increasingly use in economics. So I think there's no doubt many machine learning methods will get used more and more often. One area that's going to get a lot of attention is combining machine learning with causal inference. A big fraction of empirical microeconomics is about finding ways to exploit natural experiments, whether by using instrumental variables, regression discontinuity, matching, difference-in-difference estimators, or other methods."