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.

More about Guido Imbens: https://www.gsb.stanford.edu/faculty-research/faculty/guido-w-imbens
More about Joshua Angrist: https://economics.mit.edu/faculty/angrist
More about Isaiah Andrews: https://scholar.harvard.edu/iandrews/home

00:00 - Intro
00:18 - Potential for "personalized" causal effects
07:22 - Applications of machine learning
10:17 - Opportunities for publishing in journals
16:12 - Isaiah Andrews referees!