Matteo Bonvini

It was great to chair a panel on Veridical Data Science (vdsbook.com) 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 arxiv.org/abs/2211.09099 Just accepted on the AOAS with Laura Forastiere and Alessandra Mattei!

Come work with us at CMU! We're hiring tenure-track asst profs in the Dept of Statistics & Data Science cmu.edu/dietrich/stati…

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 arxiv.org/pdf/2409.03962

Interested in data-driven covariate adjustment? I’m presenting some recent work with @ildiazm and @SVansteelandt next week!

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 youtube.com/live/Mnum0Ox1f… stat.cmu.edu/~siva/

New paper and software alert! arxiv.org/abs/2408.14620 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. ➡️arxiv.org/pdf/2408.01630

📷 The Division of Biostatistics @UofUMedicine is building something special. Here we are at #JSM2024. We're recruiting faculty, post-docs and students. Come join us! #BiostatsElevated @UofUBiostats @UofUPHS @AmstatNews

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. onlinelibrary.wiley.com/doi/10.1111/in…

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!

The Division of Biostatistics @UofUMedicine is recruiting faculty, postdocs & PhD students at #JSM2024 this weekend. Visit our booth or DM me to connect! #BiostatsElevated @UofUBiostats @UofUPHS @AmstatNews

Very excited about this new paper by Tiger Zeng (tigerzhzeng.com) 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

this looks awesome arxiv.org/pdf/2407.18163

Really excited about this paper, w/ amazing postdoc Alex Levis awlevis.com/about/ 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! users.ugent.be/~svsteela/ @DassGhent @UGent_fwe @ResearchUGent @UGent @ERC_Research

@UnibusPluram @LarsvanderLaan3 Thank you very much, I greatly appreciate it and definitely I need to read them in detail!

@LarsvanderLaan3 @bonv3 you'll probably be interested in the above papers ^

How and why does the highly adaptive lasso break the "curse of dimensionality"?! 1/🧵

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 arxiv.org/pdf/2402.01972

I am happy to announce I have been elected to the Society for Causal Inference (sci-info.org) Board as the President-Elect! Hope to contribute to a bright future for #causalinference