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20192026
most citedGeneralizing causal inferences from randomized trials: counterfactual and graphical identification

22 citations · 75 across the 10 of their papers we have counts for

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Showing 2019Show all

6 papers · 1 filter

stat.ME2019★ 21 cited

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…

stat.ME2019★ 3 cited

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…

stat.ME2019★ 22 cited

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…

stat.ME2019

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…

stat.ME2019★ 4 cited

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…

stat.ME2019

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…