Alex Levis
@awlevis.bsky.social
Asst Prof of Biostats @ University of Pennsylvania
Center for Causal Inference
www.awlevis.com
interests: causal inference, distribution shift, machine learning, non/semiparametrics, w/ applications in EHR data & beyond
Center for Causal Inference
www.awlevis.com
interests: causal inference, distribution shift, machine learning, non/semiparametrics, w/ applications in EHR data & beyond
Reposted by Alex Levis
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.
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.
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 ...
arxiv.org
December 13, 2024 at 11:17 PM
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.
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.