It was great to chair a panel on Veridical Data Science (vdsbook.com) in Education at #JSM2025 with panelists Rebecca Barter, Bin Yu, Andrew Bray, Joshua Rosenberg, and Robin Gong! Consider integrating VDS in your next course! The textbook contains examples, code, and many exercises.

Excited to present on Thursday @eurocim.bsky.social on new work with @idiaz.bsky.social on (smooth) trimming with longitudinal data! "Longitudinal trimming and smooth trimming with flip and S-flip interventions" Prelim draft: alecmcclean.github.io/files/LSTTEs...

Rebecca Farina, Arun Kumar Kuchibhotla, Eric J. Tchetgen Tchetgen Doubly Robust and Efficient Calibration of Prediction Sets for Censored Time-to-Event Outcomes https://arxiv.org/abs/2501.04615

Happy to announce some new work with my student Kaitlyn Lee! arxiv.org/abs/2501.04871 If you're not in the know, Riesz regression is a general tool to estimate things like propensity weights without actually having to know that they are propensity weights in the first place.

My 2024 “highlights” (or what consumed my work year): 1. Double cross-fitting (arxiv.org/abs/2403.15175) 2. Calibrated sensitivity models (arxiv.org/abs/2405.08738) 3. Fair comparisons (arxiv.org/abs/2410.13522) For #3, bsky.app/profile/alec.... Below: gory details for 1 and 2 (new to bsky) 1/9

I have a new working paper with Yi Zhang & Kosuke Imai on estimating generalizable heterogeneous treatment effects (HTEs)! We account for distribution shifts in *both* individual covariates & treatment effect heterogeneity across different source sites. Details below-- arxiv.org/abs/2412.11136

Thank you Alec for leading this project, I learned a lot! This paper has a very useful study of what contrasts are feasible in situations with many treatments and positivity violations, including necessary assumptions and efficient one-step estimators. Check it out!

New-ish paper alert! arxiv.org/abs/2410.13522   We tackle the challenge of comparing multiple treatments when some subjects have zero prob. of receiving certain treatments. Eg, provider profiling: comparing hospitals (the “treatments”) for patient outcomes. Positivity violations are everywhere.

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

Should we use structure-agnostic (arxiv.org/abs/2305.04116) or smooth (arxiv.org/pdf/1512.02174) models for causal inference? Why not both? Here we propose novel hybrid smooth+agnostic model, give minimax rates, & new optimal methods arxiv.org/pdf/2405.08525 -> fast rates under weaker conditions