It was great to chair a panel on Veridical Data Science (https://t.co/8wJneocIRg) in Education at #JSM2025 with @rlbarter, @bbiinnyyuu, Andrew Bray, @jrosenberg6432, and @RuobinGong! Consider integrating VDS in your next course! The book contains examples, code, + many exercises.
Interested in #Bayesian analysis of #RDD ? Check this out https://t.co/7nVR47qNy4
Just accepted on the AOAS with Laura Forastiere and Alessandra Mattei!
I'm very proud of @AnnaGuo617 and excited to share our new manuscript on flexible #causal effect estimation in a broad class of DAGs with unmeasured confounders. #CausalTwitter
https://t.co/fso9zD9V0N
there are surprisingly many open problems when it comes to theory/methods in causal inference
check out this talk by Siva Balakrishnan for an excellent & comprehensive summary of the state of the art
https://t.co/BixhF4jImP
https://t.co/pVIslnRatf
New paper and software alert!
https://t.co/cZNy82J1kQ
Interested in modern mediation analysis methods with machine learning and multivariate mediators?
Take a look at this joint work with Richard Liu, @nickWillyamz , and @kara_rudolph
Short 🧵...
Check out our new draft on #fair risk minimization under #causal constraints, w @biosbenk. #CausalTwitter#fairML
It provides closed-form solutions, explores risk-fairness trade-offs, offers insight into mechanisms of optimal fair prediction, & more.
➡️https://t.co/tqibKrdzHh
This is how I started my journey in #RL 5 years ago. How I started my PhD.
I put all my heart into this work and at times I hated it (especially after 4 long years w 2revisions in StatSci!).
Finally out in ISR!
This was my lesson in surviving research.
https://t.co/qc8vY8QpWF
arXiv -> alphaXiv
Students at Stanford have built alphaXiv, an open discussion forum for arXiv papers. @askalphaxiv
You can post questions and comments directly on top of any arXiv paper by changing arXiv to alphaXiv in any URL!
Very excited about this new paper by Tiger Zeng (https://t.co/fwyuSRaazD)
We study causal inference w/ high-dimensional discrete confounders
We give new bias/variance results & minimax lower bounds, which characterize fundamental limits of causal inference in high dimensions
Really excited about this paper, w/ amazing postdoc Alex Levis
https://t.co/0ZdeG1TrQF
We propose conditional potential benefit (CPB) measure, ie the improvement under optimal trt vs status quo
We describe id assumptions & propose nonparametric, robust, & efficient estimators
I have several exciting openings for PhD students and postdocs in the context of my advanced ERC grant #ERCAdG on Assumption-lean (Causal) Modeling and Estimation. Please spread the news! https://t.co/zLP5Z93FyN
@DassGhent
@UGent_fwe@ResearchUGent@UGent@ERC_Research
I had a great time presenting on Efficient Plug-in (EP) Learning of Hetergeneous Treatment Effects at #ACIC
It turns out that the plug-in principle guiding #TMLE is especially beneficial for estimating causal functions like the CATE using orthogonal ML
https://t.co/CRP2Zs5cbv
I am happy to announce I have been elected to the Society for Causal Inference (https://t.co/E09ttpP2dK) Board as the President-Elect!
Hope to contribute to a bright future for #causalinference