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Charles Stein is one of my all-time favorite statisticians. And this paper from 1956 (!!) is one of my all-time favorites - it sets the foundation for modern nonparametric efficiency theory & was so so far ahead of its time: projecteuclid.org/euclid.bsmsp/1…

A short thread: It amazes me how many crucial ideas underlying now-popular semiparametrics (aka doubly robust parameter/functional estimation / TMLE / double/debiased/orthogonal ML etc etc) were first proposed many decades ago. I think this is widely under-appreciated!

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

check this out if you're interested in connections bw causal & optimal transport arxiv.org/pdf/2310.12757

Regarding causal inference: Asking people to be specific about what exactly they are trying to estimate and what assumptions they need to do that is not a scam.

These recent slides from Susan Athey and Guido Imbens at NBER are a great recent review of the most valuable data science methods I'm aware of. They cover tons of ground with lots of pointers. conference.nber.org/confer/2024/SI…

Welcome to new Assistant Professor Amanda Coston! statistics.berkeley.edu/about/news/new… #BerkeleyStats

I haven’t enjoyed the mathematics in a paper this much in a long time: arxiv.org/abs/2408.09341 Summary: an example of performing method-of-moment type analysis for high-dimensional mixtures. Joint work with my amazing colleague Jonathan Niles-Weed.

I am loving these new papers on how to select units in an experiment to improve external validity! This is like taking the end game of the experiment very seriously at the design stage! This new paper is going to the top of my reading list!

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/

Our Division is hosting its inaugural yearly Biostatistics Symposium, and this year the topic is Causal Inference! We have an exciting lineup of speakers listed below. If you are in the NYC area, please join us! Link to register in the QR below.

PSA - the main ideas behind “causal ML” and “double machine learning” go back at least 40 years Here is an estimator from a 1982 textbook that today would be called double machine learning or something similar

Edward Kennedy Charles stein actually proposed the crux of TMLE in “efficient non parametric testing and estimation” (1958) — last paragraph of section 2. Which is absolutely wild.

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

Taking a break to let Fall do its thing ... 🧡 Hope you are enjoying a restful week off, Tartans! #LifeatCMU

30 Years of Notebooks bactra.org/weblog/30-year…