New paper: Inference on Local Variable Importance Measures for Heterogeneous Treatment Effects (with Peter B. Gilbert & @alexluedtke.bsky.social). Preprint: arxiv.org/abs/2510.18843.

I'm excited to dig into this new work on numerically approximating efficient influence functions. The main idea seems to be to use a Fourier-type approximation, rather than the kernel-smoother approximation used in earlier approaches. openreview.net/pdf/cfeab45d...

📢 Postdoc opening in stats @harvardmed.bsky.social! Build variable-importance methods so patients know why a treatment is/isn't expected to work for them. Funding from PCORI (non-federal). Application review starts Dec 1. Details here: academicpositions.harvard.edu/postings/15365

New paper on generative modeling of counterfactual distributions! We give a way to answer "what if" questions with generative models. For example: what would faces look like if they were all smiling? arxiv.org/abs/2509.16842

Carlos Cinelli, Avi Feller, Guido Imbens, Edward Kennedy, Sara Magliacane, Jose Zubizarreta Challenges in Statistics: A Dozen Challenges in Causality and Causal Inference https://arxiv.org/abs/2508.17099

I want to advertise some relatively recent work which I really like, and have been fortunate to play a small role in. The paper is titled "A New Proof of Sub-Gaussian Norm Concentration Inequality" (arxiv.org/abs/2503.14347), led by Zishun Liu and Yongxin Chen at Georgia Tech.

Neat AI product for improving technical writing. Tried it on a 50 page draft of a causal ML paper. Of its top 10 comments, 4 concerned minor technical issues I'd missed (notation error, misapplication of definition, etc.). In my experience, vanilla chatbots wouldn't have caught these.

Starting to look like I might not be able to work at Harvard anymore due to recent funding cuts. If you know of any open statistical consulting positions that support remote work or are NYC-based, please reach out! 😅

I've advised 15 PhD students—10 were international students. All graduates continue advancing U.S. excellence in research and education. Cutting off this pipeline of talent would be shortsighted.

I'm a current Harvard graduate student and I found out today that I had my NSF GRFP terminated without notification. I was awarded this individual research fellowship before even choosing Harvard as my graduate school

Had a great time presenting at #ACIC on doubly robust inference via calibration Calibrating nuisance estimates in DML protects against model misspecification and slow convergence. Just one line of code is all it takes.

New paper, led by my student Alex Kokot! We study dataset compression through coreset selection - finding a small, weighted subset of observations that preserves information with respect to some divergence. arxiv.org/abs/2504.20194

The NIH overhead cut doesn't just hurt universities. It's deadly to the US economy. The US is a world leader in tech due to the ecosystem that NIH and NSF propel. It drives innovation for tech transfer, creates a highly-skilled sci/tech workforce, and fosters academic/industry crossfertilization.

Thrilled to share our new paper! We introduce a generalized autoDML framework for smooth functionals in general M-estimation problems, significantly broadening the scope of problems where automatic debiasing can be applied!

My traditional end-of-year review: some papers I read and liked in 2024. donskerclass.github.io/post/papers-...

Welcome, @danielawitten.bsky.social!

New paper! arxiv.org/pdf/2411.14285 Led by amazing postdoc Alex Levis: www.awlevis.com/about/ We show causal effects of new "soft" interventions are less sensitive to unmeasured confounding & study which effects are *least* sensitive to confounding -> makes new connections to optimal transport

👋 In Tokyo this academic year, on sabbatical at the Institute of Statistical Mathematics. In town and interested in causal ML? Would love to grab coffee and chat.

"The Elements of Differentiable Programming" link: arxiv.org/abs/2403.14606 Basically: "autodiff - it's everywhere! what is it, and how do you use it?" seems like a good resource for anyone interested in data science, machine learning, "ai," neural nets, etc #blueskai #stats #mlsky

Do you know someone applying for a PhD in stat/biostat? Suggest they submit their draft application materials for feedback/mentoring! stat.uw.edu/pre-applicat...

New paper! arxiv.org/abs/2405.08675 tldr: automatic differentiation can be used to derive efficient influence functions and construct efficient estimators.