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I am deeply honored and humbled to be recipient of an advanced ERC award #ERCAdG to spearhead research on Assumption-lean (Causal) Modeling and Estimation. @DassGhent @UGent_fwe Ghent University Research @UGent European Research Council (ERC) 1/2

Exploring fairness in AI 🤖? How do we ensure statistical/ML models adhere to fairness principles? Excited to share latest work w nima hejazi,Mark van der Laan & David Benkeser on developing methods to tackle this question! #mlTwitter #statsTwitter #causalTwitter ➡️arxiv.org/abs/2404.09847

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

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 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 Ghent University Research @UGent European Research Council (ERC)

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

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

this looks awesome arxiv.org/pdf/2407.18163

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. alphaXiv 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 Spencer Fox Eccles School of Medicine is recruiting faculty, postdocs & PhD students at #JSM2024 this weekend. Visit our booth or DM me to connect! #BiostatsElevated U of U Biostatistics U of U PHS ASA

📷 The Division of Biostatistics Spencer Fox Eccles School of Medicine is building something special. Here we are at #JSM2024. We're recruiting faculty, post-docs and students. Come join us! #BiostatsElevated U of U Biostatistics U of U PHS ASA

Check out our new draft on #fair risk minimization under #causal constraints, w David Benkeser. #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

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 🧵...

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/

Interested in data-driven covariate adjustment? I’m presenting some recent work with Iván Díaz and Stijn Vansteelandt next week!

I'm very proud of Anna Guo 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

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

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!