22 citations · 75 across the 10 of their papers we have counts for
11 papers
Exposing Vulnerability Monoculture Through Systemic Adversarial Robustness
Leo Elmecker-Plakolm, Lukasz Szpruch, Avishek Joey Bose +1
Beneath the apparent diversity of foundation models lies an increasingly homogeneous technical stack of common training pipelines, distillation methods, and data. These shared algo…
Using the target trial framework for combining information: external comparator analyses and other applications
Lawson Ung, Miguel A. Hernán, Issa J. Dahabreh
We describe how the target trial framework can be used to plan and report analyses that attempt to answer causal questions by combining information from multiple, diverse sources.…
Revisiting the g-null paradox
Sean McGrath, Jessica G. Young, Miguel A. Hernán
The parametric g-formula is an approach to estimating causal effects of sustained treatment strategies from observational data. An often cited limitation of the parametric g-formul…
Generalized interpretation and identification of separable effects in competing event settings
Mats J. Stensrud, Miguel A. Hernán, Eric J. Tchetgen Tchetgen +3
In competing event settings, a counterfactual contrast of cause-specific cumulative incidences quantifies the total causal effect of a treatment on the event of interest. However,…
Causal inference with limited resources: proportionally-representative interventions
Aaron L. Sarvet, Kerollos N. Wanis, Jessica Young +3
Investigators often evaluate treatment effects by considering settings in which all individuals are assigned a treatment of interest, assuming that an unlimited number of treatment…
Guidelines for estimating causal effects in pragmatic randomized trials
Eleanor J. Murray, Sonja A. Swanson, Miguel A. Hernán
Pragmatic randomized trials are designed to provide evidence for clinical decision-making rather than regulatory approval. Common features of these trials include the inclusion of…
Efficient and robust methods for causally interpretable meta-analysis: transporting inferences from multiple randomized trials to a target population
Issa J. Dahabreh, Sarah E. Robertson, Lucia C. Petito +2
We present methods for causally interpretable meta-analyses that combine information from multiple randomized trials to estimate potential (counterfactual) outcome means and averag…
Generalizing causal inferences from randomized trials: counterfactual and graphical identification
Issa J. Dahabreh, James M. Robins, Sebastien J-P. A. Haneuse +1
When engagement with a randomized trial is driven by factors that affect the outcome or when trial engagement directly affects the outcome independent of treatment, the average tre…
Study designs for extending causal inferences from a randomized trial to a target population
Issa J. Dahabreh, Sebastien J-P. A. Haneuse, James M. Robins +4
We examine study designs for extending (generalizing or transporting) causal inferences from a randomized trial to a target population. Specifically, we consider nested trial desig…
Towards causally interpretable meta-analysis: transporting inferences from multiple studies to a target population
Issa J. Dahabreh, Lucia C. Petito, Sarah E. Robertson +2
We take steps towards causally interpretable meta-analysis by describing methods for transporting causal inferences from a collection of randomized trials to a new target populatio…
Separable Effects for Causal Inference in the Presence of Competing Events
Mats J. Stensrud, Jessica G. Young, Vanessa Didelez +2
In time-to-event settings, the presence of competing events complicates the definition of causal effects. Here we propose the new separable effects to study the causal effect of a…