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
Such a rewarding project with @gabeloewinger.bsky.social and the team!
Participant blinding is a SUPER old idea: as late as 1784!! Yet (un)blinding seems (to me) still poorly understood, e.g., compared to confounding, selection bias. I hope our work clarifies some issues, esp. in mental health RCTs
Participant blinding is a SUPER old idea: as late as 1784!! Yet (un)blinding seems (to me) still poorly understood, e.g., compared to confounding, selection bias. I hope our work clarifies some issues, esp. in mental health RCTs
Our team of statisticians and psychedelic researchers (@awlevis.bsky.social, Mats Stensrud + Sandeep Nayak & David Yaden of @jhpsychedelics.bsky.social) developed a causal inference framework for functional unmasking in psychedelic RCTs.
See our pre-print + analysis guide/code:
tinyurl.com/yhwez25p
See our pre-print + analysis guide/code:
tinyurl.com/yhwez25p
Causal Inference in Studies with Functional Unmasking: Psychedelics and Beyond
In clinical trials for mental health treatments, functional unmasking (unblinding) is a widespread challenge wherein participants become aware of their assigned treatment. Unmasking is especially conc...
tinyurl.com
December 10, 2025 at 1:58 AM
Such a rewarding project with @gabeloewinger.bsky.social and the team!
Participant blinding is a SUPER old idea: as late as 1784!! Yet (un)blinding seems (to me) still poorly understood, e.g., compared to confounding, selection bias. I hope our work clarifies some issues, esp. in mental health RCTs
Participant blinding is a SUPER old idea: as late as 1784!! Yet (un)blinding seems (to me) still poorly understood, e.g., compared to confounding, selection bias. I hope our work clarifies some issues, esp. in mental health RCTs
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.