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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 · 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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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 · 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 · 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

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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