**Susan Athey**: The Impact of Machine Learning on Economics: "ML does not add much to questions about identification... but rather yields great improvements when the goal is semi-parametric estimation or when there are a large number of covariates relative to the number of observations...

...ML has great strengths in using data to select functional forms flexibly.... A key advantage of ML is that ML views empirical analysis as “algorithms” that estimate and compare many alternative models.... Tuning is essentially model selection, and in an ML algorithm that is data-driven. There are a whole host of advantages of this approach, including improved performance as well as enabling researchers to be systematic and fully describe the process by which their model was selected.... The recent literature at the intersection of ML and causal inference... has focused on providing the conceptual framework and specific proposals for algorithms that are tailored for causal inference. A fourth theme is that the algorithms also have to be modified to provide valid confidence intervals for estimated effects when the data is used to select the model. Many recent papers make use of techniques such as sample splitting, leave-one-out estimation, and other similar techniques to provide confidence intervals that work both in theory and in practice. The upside is that using ML can provide the best of both worlds: the model selection is data driven, systematic, and a wide range of models are considered; yet, the model selection process is fully documented, and confidence intervals take into account the entire algorithm...

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