New joint work published with @adrianraftery.bsky.social on methods for Bayesian probabilistic projections of migration

The "basic" notions of semiparametric theory, from today's arxiv.org/abs/2510.18843 from Morzywolek, Gilbert, & Luedtke

great great plenty of time to procrastinate on this

Ideally letters wouldn't be required at all, but I'd settle for them only being required at a much later stage of the process after the first stage of review

State of the stats job market: here's the cumulative number of stats tenure-track jobs posted on the UF Statistics Job Board so far, since August #statsky

New preprint out on a way to handle structural and practical violations of the overlap (also known as positivity) assumption in causal inference -- as long as the outcome is bounded, we derive simple partial identification bounds on the ATE. With @alecmcclean.bsky.social and @idiaz.bsky.social

I have a new paper out on a simple way to do causal inference with left-censored outcomes. This comes up with environmental data because measurements often have a lower limit of detection -- e.g. a chemical is undetectable below a certain level www.tandfonline.com/doi/full/10....

if you are also in the niche position of needing to run a lot of simulation studies in R on slurm clusters, I have just the thing for you: github.com/herbps10/sim...

Protect transgender scientist! 🏳️‍⚧️

Just published: Antoine Chambaz and I did the formal work to prove you can use Super Learner (also known as model stacking) for estimating quantiles, both in i.i.d. and streaming data settings www.sciencedirect.com/science/arti...

The DHS Program is officially done. As I tell my statistics students, good data is ESSENTIAL to improve the world. We can’t make things better if we don’t know the current state of things. No new DHS data collection is an incalculable loss. www.nytimes.com/2025/02/26/h...

leading off my working group talk with the traveling quack to remind everyone the healthy level of skepticism they should be bringing to the table

Looking forward to digging into this, new on ArXiv today: arxiv.org/pdf/2501.06024

This is a really nice and thought provoking preprint, and I think this point is largely true, and related to how strict causal inference is designed to estimate the effect of causes, but not causes of effects (or "reverse causation" as it's sometimes called www.stat.columbia.edu/~gelman/rese...)

This is an interesting article, and reading it made me wonder what role causal inference has in an alternative epidemiology. Causal inference gives us some nice estimators of e.g. health effects of industrial hog plants on communities, but is that really what is needed, rather than political action?

Nice commentary summarizing some issues with non-parametric Bayes, a big one being that in practice you often end up having to place priors on very abstract objects rather than on the things you may actually have prior information about projecteuclid.org/journals/bay...

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 article out -- we combined ensemble learning and adaptive conformal inference to predict emergency department arrivals in Île-de-France and provide well-calibrated prediction intervals authors.elsevier.com/a/1kEg-4xGJ-...

I started a list of articles, books, and tutorials (with BibTex!) for learning the semi-parametric efficiency theory relevant to causal inference: herbsusmann.com/2024/11/05/r... What have I missed? 🤔