Matteo Bonvini

Assistant professor in the department of statistics at Rutgers University

He/him 🏳️‍🌈

[Matteo Bonvini @bonv3]

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.

[Fabrizia Mealli @fabri_mealli]

Interested in #Bayesian analysis of #RDD ? Check this out https://t.co/7nVR47qNy4
Just accepted on the AOAS with Laura Forastiere and Alessandra Mattei!

[Edward Kennedy @edwardhkennedy]

Come work with us at CMU! We're hiring tenure-track asst profs in the Dept of Statistics & Data Science

https://t.co/A5fN18gR6H

[Razieh Nabi @raziehnabi]

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

[Kelly Van Lancker @KellyVanLancker]

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

[Edward Kennedy @edwardhkennedy]

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

[Iván Díaz @ildiazm]

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 🧵...

[Razieh Nabi @raziehnabi]

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

[Daniel Scharfstein @dscharf3]

📷

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

[Nina :Deliu @DeliuNina]

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

[Stanford AI Lab @StanfordAILab]

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!

[Daniel Scharfstein @dscharf3]

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

[Edward Kennedy @edwardhkennedy]

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

[Edward Kennedy @edwardhkennedy]

this looks awesome

https://t.co/JiTjnBlGw3

[Edward Kennedy @edwardhkennedy]

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

[Stijn Vansteelandt @SVansteelandt]

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

[Matteo Bonvini @bonv3]

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

[Lars van der Laan @LarsvanderLaan3]

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

[Alejandro Schuler @UnibusPluram]

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

1/🧵

[Fabrizia Mealli @fabri_mealli]

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