The Mixtape with Scott

In one sense, causal inference has two approaches. You can run a regression and then backwards engineer what it means. Think of Imbens and Angrist's 1994 classic Econometrica on the local average treatment effect (LATE) where they show that the Wald estimator (binary treatment, binary instrument) is the average effect for the complier subpopulation. But the other way that causal inference often runs is you start with the parameter of interest, not the regression, and then build the regressions to identify them under minimal but acceptable assumptions. In this episode of the Odd Couple, we switch from estimation to description of the causal parameters introduced in Callaway, Goodman-Bacon and Sant'Anna (2026, AER). These are the well known ATT parameter, but not the ACRT, which is the slope of the dose response curve. We also puzzle over whether our treatment is, in fact, distance measured in levels or is it distance measured as changes. Which is probably one of the values of starting with parameters: it forces you to figure out what your question is!Scott's Mixtape 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

Today will astonish and amaze because in this one, you will watch me explain the decomposition of the OLS twoway fixed effects estimator for the continuous treatment difference-in-differences! A first for podcast history I would be willing to bet! Thanks again for turning in! (Caitlin said she thought this turned out well, but your mileage may vary).Scott's Mixtape 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

Greetings from San Sebastián Spain where I am on holiday with my daughter for another couple of weeks. I have still not done any new podcasts as I realized only after I left that I did not pack my microphone. And, I didn’t want to buy a new one, and I wasn’t really 100% positive if using my Apple AirPods would work well. All of that is to say — excuses.So, this week we are going back down memory lane to an interview I did 1-2 years ago with one of my favorite young up and coming econometricians, Tymon Słoczyńsi from Brandeis University. Tymon is the author of a wonderful 2022 article on OLS models with, I’ll call it, “additive and separable” covariates under unconfoundedness. Autocorrect wanted that to be “addictive” instead of “additive”, which would’ve been a really clever Freudian slip. Tymon’s interview was one of my favorites. I know I say that about every interview, but they all feel like that, but let’s just this one really really feels that way. And I think you’ll feel the same way. One of the things I love about Tymon’s articles is how excellent the writing is. His paragraphs oftentimes feel like the kind of paragraphs that you can tell he wrote, and rewrote, and rewrote, and rewrote like a hundred times. It amazes me that English is not his first language and he writes this well. I don’t even mean this clear — I mean it’s beautiful writing. Here’s a paragraph I think is outstanding, for instance:“To aid intuition for this surprising result, recall that an important motivation for using the model in equation (1) and OLS is that the linear projection of y on d and X provides the best linear predictor of y given d and X (Angrist & Pischke, 2009). However, if our goal is to conduct causal inference, then this is not, in fact, a good reason to use this method. Ordinary least squares is “best” in predicting actual outcomes, but causal inference is about predicting missing outcomes, defined as ym = y(1) × (1− d ) + y(0) × d. In other words, the OLS weights are optimal for predicting “what is.” Instead, we are interested in predicting “what would be” if treatment were assigned differently.”A lot of his sentences are sentences that are so precise, so insightful, that I wish I could have written it. It’s superb, he’s superb, and if you haven’t listened to this, I hope you do, and if you already have listened to it, then I hope you listen to it again.Thanks again for all your support. Wish me luck as I wrap up my summer in Europe, start making my plans to move to Boston, teach new students, meet new colleagues, and make new friends. And get some new clothes to replace the ones the gentleman who stole my luggage on the train in Switzerland is now in possession of. Scott's Mixtape 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

This week’s episode of the Mixtape with Scott is a rerun of an earlier interview I did with Muhammad Akbarpour, an economic theorist at Stanford University. Muhammad tells his life story of growing up in Tehan, Iran and his long and windy road into economics and Stanford University, where he both went to grad school and is now an assistant professor. If you haven’t had a chance to listen to it or watch it, I highly recommend it again. Mohammad is one of my favorite young economists, particularly theorists, working today and I find talking to him to be really inspiring. This was one of my favorite, top 5 even, interviews I’ve had on the show so far too.Thank you again for your support. Scott's Mixtape 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

Welcome to this week’s episode of The Mixtape with Scott. Today’s podcast guest is our 127th guest on the show—Vitor Possebom, Assistant Professor in the Department of Economics at the Fundação Getulio Vargas. Vitor’s research sits at the intersection of two areas — econometrics and causal inference, and policy evaluation in Latin America, particularly Brazil. His contributions revolve around refining and extending tools for estimating causal effects in observational data, especially under common data imperfections like selection bias, measurement error, and treatment effect heterogeneity.* Sample selection and marginal treatment effects (e.g., “Identifying Marginal Treatment Effects in the Presence of Sample Selection” (Journal of Econometrics), “Crime and Mismeasured Punishment” (Review of Economics and Statistics))* Misclassification and measurement error (e.g., “Potato Potahto in the FAO-GAEZ Productivity Measures?”)* Inference and sensitivity in synthetic control methods (e.g., “Cherry Picking with Synthetic Controls”, “Synthetic Control Method: Inference, Sensitivity Analysis and Confidence Sets”)* Probability of causation in non-experimental settings (e.g., “Probability of Causation with Sample Selection”)I invited Vitor onto the podcast because of his creative contributions to causal inference, as he fits into a larger informal series I’ve been for the last several years on causal inference in general. In today’s conversation, we talk about Vitor’s path from Brazil to Yale University and then back. Vitor, thank you so much for joining us.Scott's Mixtape 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

Welcome to this week's episode of The Mixed Tape with Scott. I'm your host, Scott Cunningham. This podcast is devoted to the personal stories of living economists, diving into their lives, careers, and the fascinating paths they've walked.This week's guest is Michael Anderson, an economist from the University of California Berkeley's Department of Agricultural Resource Economics. Michael earned his PhD at MIT in 2006 under the mentorship of Josh Angrist, making him part of a broader narrative I've been exploring—the Princeton Industrial Relations Section and the influential lineage of scholars who shaped the modern credibility revolution in economics.In our conversation, we touch on Michael's rich and varied research. We discuss his insights into the returns to college athletic success, delve into his foundational work on the Perry Preschool program and the challenge of multiple inference, and explore the real-world impacts outlined in his American Economic Review paper on subway strikes and slowdowns. As always, though, this episode is much more than just research highlights—it's about Michael's journey through economics, his stories, and the experiences that have defined his path. I hope you enjoy the show!Scott's Mixtape 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

I'm excited to announce the newest episode to the podcast features a brilliant mind in econometrics and applied microeconomics: Dr. Liyang "Sophie" Sun from University College London. While Liyang has technically been a guest before, our previous conversation had been narrowly focused on econometric techniques. This time, we're shifting gears to align with the core purpose of the podcast—exploring the personal stories and journeys of living economists.Many of you know Liyang by reputation or have cited her groundbreaking work. Her 2021 paper with Sarah Abraham in the Journal of Econometrics on difference-in-differences estimated using two-way fixed effects with leads and lags was recognized as one of the recipients of the Aigner award for 2022 —a remarkable achievement. That paper in particular helped clarify exactly what we were—and weren't—measuring in difference-in-differences event studies. Beyond diagnosing issues in existing approaches, they introduced a new and more accurate estimator, known formally as the interaction-weighted estimator, but which most of us now fondly call simply “SA” (Sun and Abraham). I love that paper; it has taught me a great deal.Her research portfolio extends well beyond this, spanning instrumental variables, synthetic control methods, and other innovative approaches that have reshaped how we think about causal inference in economics.In this episode, we'll dive into Liyang’s personal journey through growing up in China, coming to the United States as a high school student, and then through college, grad school and a career as a professional economist and econometrician. She generously shares the experiences, people and discoveries that have shaped her career and research directions. It was a genuine pleasure to hear more of her story, and I believe you'll find it both enlightening and inspiring.Thank you again for all your support! Scott's Mixtape 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