How Will Machine Learning Impact Economics? (Guido Imbens, Josh Angrist, Isaiah Andrews)

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!