Herb Susmann

They also have a very neat way of deriving the efficient influence function for their infinite-dimensional parameter of interest based on Luedtke's autodiff work

Figure S1: Illustration of the basic notions of semiparametric theory

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

trying to find a way to compare against previous years, unfortunately the archive.org snapshots of the job board are spotty

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

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

Tricks you can use Identification fails: try finding bounds that hold under weaker assumptions. Non-smooth parameters: try defining a smooth approximation. Uniform inference: try a multiplier bootstrap. Having clever collaborators helps a lot!

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

Proposition 1 (non-overlap bounds)

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.

Non-overlap Average Treatment Effect Bounds by Herbert P. Susmann, Alec McClean, and Iván Díaz

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

Diagram illustrating the bounds on the true average treatment effect

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

Non-parametric treatment effect bounds for left-censored outcomes: estimating the effect of herbicide use on 2,4-D exposure Causal inference is concerned with defining and estimating the effect of a exposure on an outcome. For example, the Average Treatment Effect (ATE), a causal inference concept, is defined as the pop...

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...

Is the “well-defined intervention assumption” politically conservative?

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

Protect transgender scientists Transgender and gender nonconforming (TGnC) people are a primary target of the Trump administration. Multiple executive orders seek to erase TGnC protections; mandate denial of gender identity; and ba...

Protect transgender scientist! 🏳️‍⚧️

Quantile Super Learning for independent and online settings with application to solar power forecasting Estimating quantiles of an outcome conditional on covariates is of fundamental interest in statistics with broad application in probabilistic predicti…

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