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

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

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.

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

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

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

i offer a delightful array of asymptotically valid schemes and elixers