The Impact of Machine Learning on Economics

By Susan Athey. 2018

492 citations recorded by the source corpus.

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. Next, we brieﬂy review of some of the emerging econometric literature combining machine learning and causal inference. Finally, we overview a set of predictions about the future impact of machine learning on economics.

Subfields: Economics and Econometrics, Economics & Operations Research

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Citations in the corpus

The Effects of Social Movements: Evidence from #MeToo (2020, unknown): “…to model more complex patterns in the data, while allowing the data (rather than the analyst) to indicate whether time-series patterns within units, or cross-sectional patterns within a period, or a more complex combination, are more useful for predicting counterfactual outcome” (Athey, 2018).”
Transforming the communication between citizens and government through AI-guided chatbots (2019, background): “…used for diseases prevention, prediction of criminal activities (“predictive policing”), detection of frauds in justice, as well as for providing insights useful in order to address complex policy problems in the financial sector (Athey, 2017; Kleinberg, Ludwig, Mullainathan, & Obermeyer, 2015).”
Introduction - Platforms and Infrastructures in the Digital Age (2018, background): “This phenomenon might be partially explained by the rapid deployment of machine learning algorithms that can quickly sift through mountains of data to identify patterns (Athey 2018).”
Sparsity in Optimal Randomized Classification Trees (2020, methodology): “on is needed: Parameters D depth of the binary tree, N number of individuals in the training sample, p number of predictor variables, K number of classes, f(x i;y i)g 1iN training sample, where x i2[0;1] pand y i2f1;:::;Kg; I k set of individuals in the training sample belonging to class k, k= 1;:::”
Machine learning in energy economics and finance: A review (2019, background): “3 Electronic copy available at: https://ssrn.com/abstract=3270251 and Athey (2017) provide a non-technical overview and a review of economic/econometric applications of ML.”
AI in Finance: Challenges, Techniques, and Opportunities (2021, background): “, Bayesian networks and Markov networks), machine learning models such as for clustering, classification and semi-supervised learning, deep reinforcement learning (DRL) models, and deep neural networks [13, 21, 38, 50, 73, 133, 156].”
Forecasting: theory and practice (2020, background): “Aue et al. (2015) showed asymptotic equivalence between a FAR and a vector autoregressive (VAR) model.”
The GDP-Temperature relationship: Implications for climate change damages (2021, unknown): “In the following sections, we assess the performance of alternative models and the magnitude of model uncertainty by employing cross validation in the spirit of Athey (2017).”