How Will Machine Learning Impact Economics?

This episode is the most heated of the series! While Nobel laureates Josh Angrist and Guido Imbens agree on most topics, they sharply diverge on the potential of machine learning to impact economics. Host Isaiah Andrews steps in to referee the dispute, adding his own take on how machine learning might change econometrics.

Guido Imbens is optimistic about the potential of using machine learning to estimate “personalized casual effects” in large data sets. He laments that econometrics journals have been too rigid in their expectations, turning away many useful insights from machine learning.

Josh Angrist has a less rosy view. He has yet to see machine learning make an impact on the work he’s doing. Instead, he’s seen cases where it can be very misleading.

In this episode, they cover:

  • Machine learning's current & future roles in economics
  • Applications of machine learning
  • Opportunities for publishing in journals
  • Isaiah Andrews referees!

You can jump right to these topics on the timeline in the video above. 


 More about Guido Imbens

 More about Joshua Angrist

More about Isaiah Andrews


Teacher Resources


Verified Available Languages
  • English

Thanks to our awesome community of subtitle contributors, individual videos in this course might have additional languages. More info below on how to see which languages are available (and how to contribute more!).

How to turn on captions and select a language:

  1. Click the settings icon (⚙) at the bottom of the video screen.
  2. Click Subtitles/CC.
  3. Select a language.


Contribute Translations!

Join the team and help us provide world-class economics education to everyone, everywhere for free! You can also reach out to us at [email protected] for more info.

Submit subtitles




We aim to make our content accessible to users around the world with varying needs and circumstances.

Currently we provide:

Are we missing something? Please let us know at [email protected]


Creative Commons

Creative Commons License

This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License.
The third party material as seen in this video is subject to third party copyright and is used here pursuant
to the fair use doctrine as stipulated in Section 107 of the Copyright Act. We grant no rights and make no
warranties with regard to the third party material depicted in the video and your use of this video may
require additional clearances and licenses. We advise consulting with clearance counsel before relying
on the fair use doctrine.