S3E21: Ashesh Rambachan, Predictive Algorithms and Causal Inference, MIT
Audio is streamed directly from the publisher (api.substack.com) as published in their RSS feed. Play Podcasts does not host this file. Rights-holders can request removal through the copyright & takedown page.
Show Notes
Greetings listeners! It is a pleasure to introduce this week’s guest on the podcast, Ashesh Rambachan, an assistant professor of economics at MIT. I wanted to talk to Ashesh for two main reasons. First, because I wanted to, and second, because I was aware of some of his recent work in econometrics. His recent article on evaluating the fragility of parallel trends in difference-in-differences just came out in the Review of Economic Studies. I’m also intrigued by his work with Sendhil Mullainathan on machine learning, algorithmic fairness as well as generative AI. Having a specialist in both causal inference, artificial intelligence and machine learning is rare, so I thought sitting down with him to learn more about his story would be a lot of fun, not just for me, but for others too. With that said, here you go! I hope you enjoy the interview! Thank you again for all your support!
Scott's Substack is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.
Get full access to Scott's Mixtape Substack at causalinf.substack.com/subscribe