The Impact of Machine Learning on Economics
This paper provides an assessment of the early contributions of machine learning to economics, as well as predictions about its future contributions. It begins by briefly overviewing some themes from the literature on machine learning, and then draws some contrasts with traditional approaches to estimating the impact of counterfactual policies in economics. Next, we review some of the initial “off-the-shelf” applications of machine learning to economics, including applications in analyzing text and images. We then describe new types of questions that have been posed surrounding the application of machine learning to policy problems, including “prediction policy problems,” as well as considerations of fairness and manipulability. We present some highlights from the emerging econometric literature combining machine learning and causal inference. Finally, we overview a set of broader predictions about the future impact of machine learning on economics, including its impacts on the nature of collaboration, funding, research tools, and research questions.
I am grateful for funding from the Toulouse Network for Information Technology, the Cyber Initiative at Stanford University, the Sloan Foundation, and Office of Naval Research grant 17-1-2131.
Susan Athey serves on the boards of directors of Expedia (EXPE), Rover, Ripple, and CoinCenter. She also advises venture capital firms X/Seed Capital and NYCA Partners. She is a consulting researcher at Microsoft Research New England.