🚨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/
Want to know about the mechanisms by which a treatment affects an outcome? This paper develops tools for testing hypotheses about mechanisms under weak assumptions. Check it out!
New paper by @jondr44 and Kwon:
https://t.co/LTACLx7hJ5
#REStud#EconX#EconTwitter
1/ 🐎 Our gift for the Year of the Horse:
An AI-assisted workflow that scales reproducibility in empirical research. w/ @YangYang_Leo
Paper: https://t.co/fslLN0zTQO
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/LfqY4XiqjT
🔎Updated paper: https://t.co/WFrpA6iwXn
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
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 👇
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) https://t.co/FVCV6ncJw8
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