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

RieszBoost: Gradient Boosting for Riesz Regression Answering causal questions often involves estimating linear functionals of conditional expectations, such as the average treatment effect or the effect of a longitudinal modified treatment policy. By ...

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

Minimax Regret Estimation for Generalizing Heterogeneous Treatment Effects with Multisite Data To test scientific theories and develop individualized treatment rules, researchers often wish to learn heterogeneous treatment effects that can be consistently found across diverse populations and co...

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!

Fair comparisons of causal parameters with many treatments and positivity violations Comparing outcomes across treatments is essential in medicine and public policy. To do so, researchers typically estimate a set of parameters, possibly counterfactual, with each targeting a different ...

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

Thank you very much, Edward and Alec, for your very generous words. I feel extremely lucky to have the chance to keep learning from you on a regular basis :))