The Impact of Machine Learning on Economics: What Machine Learning Can (and Cannot) Do for Economic Research

By Goicoechea, Elena Badillo | Chicago Policy Review (Online), January 21, 2019 | Go to article overview

The Impact of Machine Learning on Economics: What Machine Learning Can (and Cannot) Do for Economic Research


Goicoechea, Elena Badillo, Chicago Policy Review (Online)


Machine learning (ML) is most commonly understood as a set of computational techniques applied to big datasets in order to make granular predictions for businesses, from advertising to fraud detection to user recommendations. Yet another, perhaps less appreciated, application comes from academia, where social scientists have slowly but steadily begun leveraging ML techniques to gain new insights from data.

In a recent paper from the National Bureau of Economic Research (NBER), Susan Athey provided a useful assessment of the contributions of ML to economics, summarizing emerging econometric literature combining ML and causal inference, before drawing broader conclusions about the impact of ML on economics as a field.

First, Athey culled through the plethora of buzzwords related to ML to establish a practical definition for economists and policy analysts. She defined ML as “a field that develops algorithms designed to be applied to data sets, with the main areas of focus being prediction, classification and clustering tasks.” For the author, the key was a clear distinction between the goals of ML techniques and the goals of traditional econometric methods of causal inference. In econometrics, Athey wrote, the primary aim is typically to uncover a clean, causal relationship between the outcome variable and another variable of interest. As such, econometricians established a solid empirical framework for answering questions regarding the impact of various policy changes on particular populations. In contrast, ML techniques are not designed to identify causal relationships between variables; rather, their purpose is to make accurate predictions. Athey argued that this constitutes the crucial difference between econometrics and ML: The former aims to establish causality, while the latter aims to produce accurate and actionable predictions.

Fortunately, this does not mean both frameworks cannot work together. In fact, Athey argued that there is much to gain from implementing both frameworks side by side. …

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