I love living in a city full of immigrants and tons and tons of people who are not at all like me and not like each other. It makes us all better and it makes our city better. I know I’m preaching to the choir by saying this on the lib app but I sometimes just get so overwhelmed by how special it is

my interest in putting bounds on things now

some of the tricks we found useful -- the last bullet especially, I learned a lot from working closely with @alecmcclean.bsky.social on this

what's neat about our approach is that you can vary the propensity score threshold that defines the overlap and non-overlap population, and then choose the threshold that yields the smallest bounds -- with frequentist guarantees

The idea is very simple: we divide the population into a part in which overlap is satisfied, and a part in which overlap is violated. The non-overlap part is the one that poses problems, so we just apply worst-case bounds on the ATE in that subpopulation.

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

a related tip i've heard for talks is to use author + year + journal abbreviation for references on the slides (e.g. Robins 1995 JASA), makes it easier for people to find what you're talking about

The paper includes a friendly (I hope) introduction to causal inference and TMLE, and has sample R code you can use to run this type of analysis

The insight is that while you can't point identify a treatment effect when the outcome is left-censored, it's possible to derive bounds on the true average treatment effect. It turns out you can estimate these bounds using standard causal inference methods like TMLE

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....

the setup in this template uses slurm job arrays to spin up a bunch of workers, each of which then simulates some data, runs your estimators, saves the results in a cache directory, and then helps you collect all the results and generate tables/figures

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...

about that: www.sciencedirect.com/science/arti...

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...

i offer a delightful array of asymptotically valid schemes and elixers

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...)

that is, it isn't narrowly the "well-defined intervention assumption" that restricts the scope of inquiry and action, it's the overall project of "risk factor epidemiology" that is limiting

This paper, a favorite, gestures at similar ideas -- although in my opinion it is a bit too wrapped up in the specifics of causal inference methodology linkinghub.elsevier.com/retrieve/pii...