4 citations · 5 across the 8 of their papers we have counts for
8 papers · 1 filter
Causal Effects of Modified Treatment Policies under Positivity Violations: A Partial Identification Approach
Taehyeon Koo, Elizabeth A. Stuart, Kara E. Rudolph +1
Modified treatment policies (MTPs) are interventions based on each individual's natural treatment value. We study mean outcomes under MTPs for continuous treatments, including expo…
Modified treatment policies that depend on the natural history of treatment
Iván Díaz, Nicholas T. Williams, Paweł Morzywołek +1
Longitudinal modified treatment policies (LMTP) are a class of interventions that allow the definition, identification, and estimation of causal effects in general settings, such a…
Considerations for the Integration of Randomized Controlled Trials and Real-World Data
Sky Qiu, Charles Barr, Lauren Dang +18
As clinical decision-making increasingly moves toward individualized and context-specific treatment recommendations, reliance on any single evidence source, randomized or observati…
crumble: A comprehensive framework for modern causal mediation analysis with intermediate confounding
Richard Liu, Nicholas T. Williams, Kara E. Rudolph +1
Causal mediation analysis is widely used to investigate how causal effects operate through specific pathways linking treatments or exposures to outcomes. Recently, \texttt{crumble}…
Transporting results from a trial to an external target population when trial participation impacts adherence
Rachael K. Ross, Ivan Diaz, Amy J. Pitts +2
Randomized clinical trials are considered the gold standard for informing treatment guidelines, but results may not generalize to real-world populations. Generalizability is hinder…
A Cautionary Tale on Integrating Studies with Disparate Outcome Measures for Causal Inference
Harsh Parikh, Trang Quynh Nguyen, Elizabeth A. Stuart +2
Data integration approaches are increasingly used to enhance the efficiency and generalizability of studies. However, a key limitation of these methods is the assumption that outco…
Who Are We Missing? A Principled Approach to Characterizing the Underrepresented Population
Harsh Parikh, Rachael Ross, Elizabeth Stuart +1
Randomized controlled trials (RCTs) serve as the cornerstone for understanding causal effects, yet extending inferences to target populations presents challenges due to effect hete…
Transporting treatment effects from difference-in-differences studies
Audrey Renson, Ellicott C. Matthay, Kara E. Rudolph
Difference-in-differences (DID) is a popular approach to identify the causal effects of treatments and policies in the presence of unmeasured confounding. DID identifies the sample…