iiinova — where your digital self keeps evolving

Herb Susmann @herbps10.bsky.social · 11/06/2026

Maybe try Four Seasons Total Landscaping?

Herb Susmann @herbps10.bsky.social · 11/02/2026

Check out my talk at the Online Causal Inference Seminar last week on a practical way to deal with positivity violations using bounds 👇 #causalsky

youtube.com

Young Researchers' Seminar: Herb Susmann and Juraj Bodík

YouTube video by Online Causal Inference Seminar

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Herb Susmann @herbps10.bsky.social · 05/12/2025

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

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Herb Susmann @herbps10.bsky.social · 22/10/2025

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

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Herb Susmann @herbps10.bsky.social · 22/10/2025

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

Figure S1: Illustration of the basic notions of semiparametric theory

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Herb Susmann @herbps10.bsky.social · 17/10/2025

great great plenty of time to procrastinate on this

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Herb Susmann @herbps10.bsky.social · 16/10/2025

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

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Herb Susmann @herbps10.bsky.social · 11/10/2025

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

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Herb Susmann @herbps10.bsky.social · 11/10/2025

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

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Reposted by Herb Susmann

Kath Barbadoro @kathbarbadoro.bsky.social · 08/10/2025

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

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Herb Susmann @herbps10.bsky.social · 25/09/2025

my interest in putting bounds on things now

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Herb Susmann @herbps10.bsky.social · 25/09/2025

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

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!

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Herb Susmann @herbps10.bsky.social · 25/09/2025

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

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Herb Susmann @herbps10.bsky.social · 25/09/2025

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.

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Herb Susmann @herbps10.bsky.social · 25/09/2025

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

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

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Herb Susmann @herbps10.bsky.social · 05/09/2025

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

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Herb Susmann @herbps10.bsky.social · 03/09/2025

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

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Herb Susmann @herbps10.bsky.social · 03/09/2025

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

Diagram illustrating the bounds on the true average treatment effect

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Herb Susmann @herbps10.bsky.social · 03/09/2025

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....

tandfonline.com

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...

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Herb Susmann @herbps10.bsky.social · 26/08/2025

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

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Herb Susmann @herbps10.bsky.social · 26/08/2025

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...

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Herb Susmann @herbps10.bsky.social · 13/05/2025

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...

sciencedirect.com

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…

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Reposted by Herb Susmann

Heather Randell @heatherrandell.bsky.social · 27/02/2025

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...

nytimes.com

Trump Administration Ends Global Health Research Program

The Demographic and Health Surveys were the only sources of reliable information in many countries on metrics such as mortality, nutrition and education.

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Herb Susmann @herbps10.bsky.social · 16/01/2025

i offer a delightful array of asymptotically valid schemes and elixers

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Herb Susmann @herbps10.bsky.social · 16/01/2025

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

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Herb Susmann @herbps10.bsky.social · 13/01/2025

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

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Herb Susmann @herbps10.bsky.social · 08/01/2025

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...)

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Herb Susmann @herbps10.bsky.social · 28/12/2024

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

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Herb Susmann @herbps10.bsky.social · 28/12/2024

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...

linkinghub.elsevier.com

Redirecting

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Herb Susmann @herbps10.bsky.social · 28/12/2024

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?

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Herb Susmann @herbps10.bsky.social · 28/12/2024

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...

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Reposted by Herb Susmann

Alec McClean @alecmcclean.bsky.social · 13/12/2024

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.

arxiv.org

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 ...

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Herb Susmann @herbps10.bsky.social · 09/12/2024

Adaptive conformal inference is a nice method for calibrating prediction intervals for online (e.g. time-series) data, with strong finite-sample guarantees -- see our overview paper, which describes our R package computo.sfds.asso.fr/published-20...

computo.sfds.asso.fr

AdaptiveConformal: An R Package for Adaptive Conformal Inference

Conformal Inference (CI) is a popular approach for generating finite sample prediction intervals based on the output of any point prediction method when data are exchangeable. Adaptive Conformal Infer...

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Herb Susmann @herbps10.bsky.social · 09/12/2024

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-...

authors.elsevier.com

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Herb Susmann @herbps10.bsky.social · 27/11/2024

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? 🤔

herbsusmann.com

Resources for Learning Semi-parametric Theory | Herb Susmann

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