Susan Athey Discusses Machine Learning and Its Impact on Economics

By TAP Staff Blogger

Posted on July 19, 2019


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“Off-the-shelf ML [machine learning] methods, for tasks such as prediction, classification and clustering, will become pervasive.” – Stanford economist Susan Athey

 

In the latest issue of The Toulouse School of Economics Magazine, Susan Athey discusses how machine learning (ML) is transforming economics. The Toulouse Magazine asked Professor Athey to address the following questions:

 
  • What impact will ML have on research and policy?
  • Can ML improve scientific credibility?
  • What are some of the most promising ML innovations?
  • What effect will ML have on the way economists work?
 

Below are a few excerpts from “Machine Learning and Economics” (The Toulouse School of Economics Magazine, summer 2019, pages 22-23):

 

The Impact of Machine Learning on Research and Policy

 

Off-the-shelf ML methods, for tasks such as prediction, classification and clustering, will become pervasive. There have already been a number of successful policy applications. Examples by Harvard economist Sendhil Mullainathan and coauthors include predicting whether an elderly patient will die within a year to determine whether to do a hip operation. Harvard economists Edward Glaeser, Andrew Hillis, Scott Kominers, and Michael Luca have helped cities to predict health-code violations in restaurants, in order to better allocate inspector resources. Using ML together with satellite imagery and street maps can predict economic quantities such as poverty, safety, and home values.

 

Machine Learning and Scientific Credibility

 

When appropriate and properly applied, ML methods provide a rigorous and systematic approach. By selecting the best model for the data, ML algorithms prevent a researcher from cherry-picking the models that give the most appealing results. Transparency is improved, since the researcher can fully describe the algorithm and model selection can be easily replicated.

 

ML enables an increased emphasis on stability and robustness. Large tech firms release new algorithms every week, and conduct thousands of experiments per year. E-commerce firms and even physical stores change prices regularly, and scanners and transaction logs can provide this data. So we have lots of ways to test the credibility of models and counterfactual predictions.

 

How Economists’ Work Will Be Impacted by Machine Learning

 

We will see increased adoption of collaboration tools; for example, my generalized random forest software is available as an open-source package (github.com/grf-labs/grf). There will be an increased emphasis on documentation and reproducibility, even as some data sources remain proprietary. “Fake” data sets will allow others to replicate analysis.

 

All disciplines will gain a much greater ability to intervene in the environment in a way that facilitates measurement and causal inference. When people get most of their information digitally across areas like health, education, shopping, and travel, there will be opportunities to experiment with that information provision to learn how to make it more efficient.

 

We will also see more interdisciplinary majors. The curricula will evolve from a truly engineering base to being more problem-solving. That will increase the demand for economists generally, but also change the way we teach and research.

 

Read the full article: “Machine Learning and Economics” (The Toulouse School of Economics Magazine, summer 2019, pages 22-23)

 

Furthermore, Professor Athey provides an assessment of the early contributions of machine learning to economics, as well as predictions about its future contributions in a recent paper, “The Impact of Machine Learning on Economics” (The Economics of Artificial Intelligence: An Agenda, University of Chicago Press, May 2019).

 

Susan Athey is the Economics of Technology Professor at Stanford Graduate School of Business. She also is Professor of Economics (by courtesy), School of Humanities and Sciences, and Senior Fellow, Stanford Institute for Economic Policy Research. Professor Athey’s current research focuses on the economics of the Internet, marketplace design, auction theory, the statistical analysis of auction data, and the intersection of computer science and economics. She is an expert in a broad range of economic fields – including industrial organization, econometrics, and microeconomic theory – and has used game theory to examine firm strategy when firms have private information.

 

Professor Athey was awarded the Jean-Jacques Laffont Prize in Toulouse, France in 2016. The annual award recognizes an internationally renowned economist whose research is in the spirit of the work undertaken by Professor Jean-Jacques Laffont, combining both the theoretical and the empirical. In 2007, Professor Athey was named the first female recipient of the American Economic Association’s prestigious John Bates Clark Medal, awarded every other year to the most accomplished American economist under the age of 40 “adjudged to have made the most significant contribution to economic thought and knowledge.”

 


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