Shaikh Tanvir Hossain
@ShaikhTanvirHo2Doctoral student of Economics at TU Dortmund, studying Econometrics and Causality.
Similar User
@krizna_b
@Parasto89264726
@mohammed_sabri4
@kikosteve
@PierreLGagnon
@Ersozcem
@Simone00688969
'Cluster-Adaptive Network A/B Testing: From Randomization to Estimation', by Yang Liu, Yifan Zhou, Ping Li, Feifang Hu. jmlr.org/papers/v25/22-… #cluster #clusters #adaptive
I haven't had a chance to read yet, but looks like @StefanWager wrote the causal inference book I've always wanted! web.stanford.edu/~swager/causal…
Excited about the fantastic program ahead! Presenting here our work on the design of clusters/cluster experiments with network spillovers
Amazing network economics workshop is coming up in the Twin Cities @UMNISyE sites.google.com/umn.edu/nse202… with @VivianoDavide @ben_golub Matt Jackson, and many more. sites.google.com/umn.edu/nse202… And, @korenmiklos will present our paper on collaboration in open-source software.
With the Spring semester starting at @MIT, I'm excited to be teaching two new courses on machine learning and economics with @m_sendhil (one for PhD students and one for undergraduates). We'll be posting lecture slides here for anyone to follow along: economics.mit.edu/people/faculty…
Hi #EconTwitter! 📈 I'm very excited to share that our paper on bootstrapping asymptotically biased estimators - joint with @MortenEcon, @e_zanelli & @silviag45129862 - is now openly accessible on the JASA website! As always, we're eager for your comments and suggestions! ⭐️…
This is a sensational find, BTW.
Have you come across Pollard's stuff? stat.yale.edu/~pollard/Books/ Looks like he would appreciate help (update Oct 23) editing his new book: stat.yale.edu/~pollard/Books…
Hi #EconTwitter! 📈 On the hunt for the ultimate guide on linear models in #statistics and #econometrics? Check out these freshly released notes by @pengding00 (@UCBerkeley), now available on @ArXiv Covering a lot, from the fundamentals to more complex extensions, they are an…
Hi #EconTwitter! Interested in #MachineLearning methods for causal inference? The Summer Institute in Computational Social Science has a lot of interesting videos and tutorials on the subject. Check it out! 👇 Links: Playlist on DL, text analysis, etc:…
Hi #EconTwitter! 📊 Embarking on an early stage of your #Econ PhD and need a foundational guide in #statistics? Dive into this "old school" book by John Marden (@UofIllinois). With a clear, rigorous presentation, it delves into: (i) distribution theory, (ii) inference,…
O people, consider this parable: Those upon whom you call, besides God, cannot create even a fly, if they all met together for that purpose. And if the fly snatches away anything from them, they would not be able to recover that from it. Powerless are those who call and those…
Hi #EconTwitter! 📚 Interested in the development of the #econometrics of panel data methods and their significance in causal inference? Check out👇this in-progress book by @CdeChaisemartin (@SciencesPo) & Xavier D'Haultfœuille (CREST)! From debunking myths surrounding TWFE…
Each year around this time, I update github.com/matloff/worthy…, my advice to undergrad students in Computer Science, Data Science and Statistics, esp. first-year students. ("What no one else will tell you")
Math teacher Thomas Garrity knows how to get the full attention of the students
Susan Athey speaking in 2010 about balancing her personal and professional lives:
In a world where you can be anything, be kind.. 😊
Love every place on earth, for it was all created by Allah. The whole world is our masjid and we should share the message of Islam with all nations in the best possible way. #Islam #dawah
Hi #EconTwitter! Want to explore how #MachineLearning can be useful for your #Econometrics? 📈 These cool notes 📚 by Christophe Gaillac (@UniofOxford) & Jérémy L’Hour (CREST) are among my top picks! A must-have for econometricians & applied researchers. Check them out!👇
Fed up cat mom finally finds her kitten.. 😅
United States Trends
- 1. Chiefs 125 B posts
- 2. Josh Allen 49,4 B posts
- 3. 49ers 39 B posts
- 4. Niners 7.679 posts
- 5. Mahomes 34,8 B posts
- 6. Geno 33,2 B posts
- 7. Super Bowl 1.261 posts
- 8. Bo Nix 15,4 B posts
- 9. #KCvsBUF 20,3 B posts
- 10. WWIII 96,9 B posts
- 11. Falcons 20 B posts
- 12. Seahawks 26,9 B posts
- 13. Broncos 33,6 B posts
- 14. Kyle 46 B posts
- 15. Chargers 17,2 B posts
- 16. Steelers 128 B posts
- 17. Paige 19,2 B posts
- 18. 72 Dolphins 1.159 posts
- 19. Ravens 87,5 B posts
- 20. Bears 118 B posts
Something went wrong.
Something went wrong.