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

Have you ever been confronted to a hairy, unwieldy, ugly inequality involving sums of powers of sequences? Something you'd like to check, but don't have the patience, time, or rough paper to deal with? Something you'd like to automatize? Well, Hölder my beer—you're in luck! 1/

Night Beds - "Stormin'" youtu.be/nq4DK2L3vdk?fe… via YouTube

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

It was a pleasure to share my thoughts on statistics and AI in this interview.

"Position: Considerations for Differentially Private Learning with Large-Scale Public Pretraining," with Florian Tramèr & Nicholas Carlini got an #ICML2024 best paper award! x.com/thegautamkamat… 🧵: the personal side of this research, emotional high & lows, & more 👇 1/n

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.

Amazing presentations by Susan Athey and guido imbens Designing Complex Experiments youtube.com/watch?v=I6GyDW…

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!

pretty interesting-looking set of presentations / interviews: ledaliang.github.io/journalclub/in…

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/