Document worth reading: “Machine Learning Methods Economists Should Know About”

We deal with the relevance of the present Machine Learning (ML) literature for economics and econometrics. First we deal with the variations in targets, methods and settings between the ML literature and the usual econometrics and statistics literatures. Then we deal with some explicit methods from the machine finding out literature that we view as important for empirical researchers in economics. These embody supervised finding out methods for regression and classification, unsupervised finding out methods, along with matrix completion methods. Finally, we highlight newly developed methods on the intersection of ML and econometrics, methods that normally perform larger than each off-the-shelf ML or additional standard econometric methods when utilized to particular programs of points, points that embody causal inference for widespread remedy outcomes, optimum protection estimation, and estimation of the counterfactual influence of worth modifications in shopper different fashions. Machine Learning Methods Economists Should Know About