Machine Learning in Economics

Seems like economists got a powerful weapons at their hand to investigate some critical and emerging issues. Following is from Quartz:

Harvard’s Sendhil Mullainathan is one of a small number of economists who has delved into the world of machine learning, the subfield of computer science concerned with using algorithms to learn from data. His research, along with the work of Stanford’s Susan Athey, suggests that while machine learning may not revolutionize economics, it will greatly expand its possibilities, and more economists should be using it.

You can read the Mullainathan’s paper from here.

Susan Athey made some broader Predictions about the impact of Machine Learning on economics:

She predicts that a number of changes will emerge, summarized as follows:
1. Adoption of off-the-shelf ML methods for their intended tasks (prediction, classification, and
clustering, e.g. for textual analysis)
2. Extensions and modifications of prediction methods to account for considerations such as
fairness, manipulability, and interpretability
3. Development of new econometric methods based on machine learning designed to solve traditional
social science estimation tasks
4. No fundamental changes to theory of identification of causal effects
5. Incremental progress to identification and estimation strategies for causal effects that exploit
modern data settings including large panel datasets and environments with many small
experiments
6. Increased emphasis on model robustness and other supplementary analysis to assess credibility
of studies
7. Adoption of new methods by empiricists at large scale
8. Revival and new lines of research in productivity and measurement
9. New methods for the design and analysis of large administrative data, including merging
these sources and privacy-preserving methods
10. Increase in interdisciplinary research
11. Changes in organization, dissemination, and funding of economic research
12. Economist as engineer engages with firms, government to design and implement policies in
digital environment
13. Design and implementation of digital experimentation, both one-time and as an ongoing
process, including “multi-armed bandit” experimentation algorithms, in collaboration with
firms and government
14. Research on developing high-quality metrics that can be measured quickly, in order to facilitate
rapid incremental innovation and experimentation
15. Increased use of data analysis in all levels of economics teaching; increase in interdisciplinary
data science programs
16. Research on the impact of AI and ML on economy