Our MS in Economics program application deadline has been extended until Friday, March 14th. Take a closer look at this STEM-designated, 1 year MS program 📈📊 Emory LGS Emory University ➡️economics.emory.edu/masters/index.…

A primer on optimal transport for causal inference with observational data. arxiv.org/abs/2503.07811

Difference-in-Differences Designs: A Practitioner's Guide. arxiv.org/abs/2503.13323

This paper has been in the making for so long that eight children across the co-author group were born between the time we started and now! But it is finally out, and you can check it at arxiv.org/pdf/2503.13323

We from Emory Economics are excited to host the "Econometrics in Emory: Causal Inference with Panel Data." 📅 May 2–3, 2025 🔗 econometricsatemory.com We aim to strengthen the relationship between academia and industry researchers so we all learn from each other!

An Introduction to Double/Debiased Machine Learning. arxiv.org/abs/2504.08324

Busy Easter weekend! 🥇 Won 1st place for my poster at the Student Research Fair at Emory Economics 📈 Then attended the 4th Georgia Econometrics Workshop Endless thanks to Pedro H. C. Sant'Anna for the support and guidance that made it all happen!

How can we design algorithms that maximize social welfare, rather than profits? This paper merges multi-armed bandits and adversarial learning with optimal tax theory and welfare economics. Maximilian Kasy Nicolò Cesa-Bianchi @Rcolomboni econometricsociety.org/publications/e…

🚨 This week! 🚨 Our workshop Econometrics at Emory: Causal Inference with Panel Data is happening May 2–3, 2025! We have a stellar lineup of speakers from both academia and industry, with Guid Imbens leading them as our Keynote Speaker.

Better Understanding Triple Differences Estimators. arxiv.org/abs/2505.09942

When your friend puts out a paper a day after you write a post 🤡 no worries, Marcelo Ortiz-Villavicencio is getting an🌟exclusive🌟one arxiv.org/pdf/2505.09942

The nicest way to finish the week is with a new post :) New tools for empiricists: better DDD estimators, distributional DiD with staggered timing, using causal diagrams to assess parallel trends, and how counterfactuals work differently in CI vs XAI substacktools.com/sharex/hAcOE8-s

Excited to be presenting at Café CIEC this Friday! I’ll be presenting: Better Understanding Triple Differences Estimators(arxiv.org/abs/2505.09942) 🗓️ May 30 | 🕒 11:30 AM (GMT-5) Registros en el link abajo 👇

When fitting synthetic controls, simultaneously balancing multiple outcomes can improve performance. Just Accepted new paper by Liyang Sun, Eli Ben-Michael (Eli Ben-Michael), and Avi Feller (Avi Feller) zurl.co/fPGWR

Robust and Agnostic Learning of Conditional Distributional Treatment Effects. arxiv.org/abs/2205.11486

We have a different post today I've had to defend using ML in my own work, so I decided to write down my case for it - for students, colleagues, skeptics, and for anyone who believes we share the goal of solving problems with the best tools available :) 🔗substacktools.com/sharex/21_ngMnW

If you've been wondering how to explore the informational content of your Parallel Trends assumptions to get the most precise DiD and ES estimator possible, we have some good news for you!! Our new DiD paper is out: arxiv.org/abs/2506.17729 😎Get along for a brief overview😎 1/

We have some updates on our DDD paper. In our latest version, we have introduced three new applications and an open-source R package to facilitate the usage of all our DDD tools! 🚀R package: marcelortiz.com/triplediff/ 🔎Updated paper: arxiv.org/abs/2505.09942

Interactive, Grouped and Non-separable Fixed Effects: A Practitioner's Guide to the New Panel Data Econometrics. arxiv.org/abs/2507.19099