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1 Unlocking Your Hidden Genius: How to Harness Your Innate Talents with Betsy Wills & Alex Ellison | Ep. 289 32:08
S3E21: Ashesh Rambachan, Predictive Algorithms and Causal Inference, MIT
Manage episode 423043287 series 3343922
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
124 episod
Manage episode 423043287 series 3343922
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
124 episod
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1 S4E18: Liyang Sun, Econometrics, University of College London 1:07:26

1 S4E17: Nathan Nunn, Economic History and Development, University of British Columbia 1:11:29

1 S4E16: Jérémy L'Hour, Econometrics and Machine Learning, Capital Fund Management and CREST 1:14:52

1 S4E15: Dmitry Arkhangelsky, Econometrics and Machine Learning, CEMFI 1:19:27

1 S4E12: Diane Whitmore Schanzenbach, Labor, Northwestern University 1:18:08

1 S4E11: Marie Connolly, Labor Economist, Université du Québec à Montréal 1:18:42

1 S4E10: Ted Joyce, Health Economist, CUNY 1:14:00

1 S4E9: Francine Blau, Gender and Labor Economics, Cornell University 1:14:16

1 S4E8: Jann Spiess, Machine Learning and Causal Inference, Stanford 1:57:27

1 S4E7: Elizabeth Cascio, Labor Economist, Dartmouth 1:19:58

1 S4E6: Timothy Bartik, Labor Economics, Upjohn Institute 1:38:01

1 S4E5: Miikka Rokkanen, Consumer Behavior Analytics, Amazon 1:20:19

1 S4E4: Maya Rossin-Slater, Health Economist, Stanford 1:20:47

1 S4E3: Mohammad Akbarpour, Microeconomic Theory, Stanford 1:27:58

1 S4E2: N. Greg Mankiw, Macroeconomics, Harvard 1:12:04
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