Marcelo Ortiz-Villavicencio
@marcelortizv
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🇪🇨 Econ. Ph.D. Candidate @EmoryEconomics | Econometrics, Causal Inference and Machine Learning
Atlanta, GA
Joined May 2010
🚀 Here we go! Our new paper with @pedrohcgs just dropped. We unpack DDD designs to better understand what's going on— Thread below has the scoop. 👇🧵
🚨New paper alert🚨 My paper with @marcelortizv on "Better Understanding Triple Differences Estimators" is finally out. 📰 https://t.co/3eJRn7m5JU Main msg: Nuances in DDD designs challenge the practice of generally viewing DDD as the diff btw two DiDs. We can do better! 1/
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🎓 Exciting News for current and future @EmoryEconomics Scholars! We’re happy to announce an increase in the stipend for current and incoming @laneygradschool PhD students! $42K per year for 5 years + tuition waiver, 100% health insurance premium, and more💰Learn more and apply⬇️
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Interactive, Grouped and Non-separable Fixed Effects: A Practitioner's Guide to the New Panel Data Econometrics.
arxiv.org
The past 20 years have brought fundamental advances in modeling unobserved heterogeneity in panel data. Interactive Fixed Effects (IFE) proved to be a foundational framework, generalizing the...
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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: https://t.co/LDfpZihQvN 🔎Updated paper:
arxiv.org
Triple Differences (DDD) designs are widely used in empirical work to relax parallel trends assumptions in Difference-in-Differences (DiD) settings. This paper highlights that common DDD...
🚨New paper alert🚨 My paper with @marcelortizv on "Better Understanding Triple Differences Estimators" is finally out. 📰 https://t.co/3eJRn7m5JU Main msg: Nuances in DDD designs challenge the practice of generally viewing DDD as the diff btw two DiDs. We can do better! 1/
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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: https://t.co/kNYgOdBoqa 😎Get along for a brief overview😎 1/
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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 :) 🔗 https://t.co/bZyuq8MY4V
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Robust and Agnostic Learning of Conditional Distributional Treatment Effects.
arxiv.org
The conditional average treatment effect (CATE) is the best measure of individual causal effects given baseline covariates. However, the CATE only captures the (conditional) average, and can...
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When fitting synthetic controls, simultaneously balancing multiple outcomes can improve performance. Just Accepted new paper by Liyang Sun, Eli Ben-Michael (@EliBenMichael), and Avi Feller (@AviFeller)
direct.mit.edu
Abstract. When there are multiple outcome series of interest, Synthetic Control analyses typically proceed by estimating separate weights for each outcome. In this paper, we instead propose estimat...
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Excited to be presenting at Café CIEC this Friday! I’ll be presenting: Better Understanding Triple Differences Estimators( https://t.co/LH2lSo5yP4) 🗓️ May 30 | 🕒 11:30 AM (GMT-5) Registros en el link abajo 👇
arxiv.org
Triple Differences (DDD) designs are widely used in empirical work to relax parallel trends assumptions in Difference-in-Differences (DiD) settings. This paper highlights that common DDD...
Café CIEC! Acompáñanos en este espacio virtual para aprender, compartir y conversar sobre: 🧠 Better Understanding Triple Differences Estimators 📢 Expositor: Marcelo Ortiz Villavicencio 🗓️ 30/May/2025 | 🕒11:30 | 💻Vía Zoom 🔗 Regístrate: https://t.co/cDWVmt3qnG
#CaféCIEC
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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 https://t.co/5v5bNOyo55
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When your friend puts out a paper a day after you write a post 🤡 no worries, @marcelortizv is getting an🌟exclusive🌟one https://t.co/wa1pYqURad
We have a new post, covering: - Continuous treatments with stayers – Sample selection and missing outcomes – Inference when treated units are few + video lectures with Clément de Chaisemartin :) (link in the replies)
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Better Understanding Triple Differences Estimators.
arxiv.org
Triple Differences (DDD) designs are widely used in empirical work to relax parallel trends assumptions in Difference-in-Differences (DiD) settings. This paper highlights that common DDD...
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🚨 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.
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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. @maxkasy @NicoloCB @Rcolomboni
https://t.co/0lUWkWZWl0
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Busy Easter weekend! 🥇 Won 1st place for my poster at the Student Research Fair at @EmoryEconomics 📈 Then attended the 4th Georgia Econometrics Workshop Endless thanks to @pedrohcgs for the support and guidance that made it all happen!
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An Introduction to Double/Debiased Machine Learning.
arxiv.org
This paper provides a practical introduction to Double/Debiased Machine Learning (DML). DML provides a general approach to performing inference about a target parameter in the presence of nuisance...
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We from @EmoryEconomics are excited to host the "Econometrics in Emory: Causal Inference with Panel Data." 📅 May 2–3, 2025 🔗 https://t.co/bJIAVl0Baz We aim to strengthen the relationship between academia and industry researchers so we all learn from each other!
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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 https://t.co/ZFSNCPrXsg
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Difference-in-Differences Designs: A Practitioner's Guide.
arxiv.org
Difference-in-differences (DiD) is arguably the most popular quasi-experimental research design. Its canonical form, with two groups and two periods, is well-understood. However, empirical...
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A primer on optimal transport for causal inference with observational data.
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