We consider methods for causal inference in randomized trials nested within cohorts of trial-
eligible individuals, including those who are not randomized. We show how baseline …

When treatment effect modifiers influence the decision to participate in a randomized trial,
the average treatment effect in the population represented by the randomized individuals …

Causal effects are defined as comparisons of potential outcomes under different treatments
on a common set of units. Observed values of the potential outcomes are revealed by the …

In this article, we examine study designs for extending (generalizing or transporting) causal
inferences from a randomized trial to a target population. Specifically, we consider nested …

Drawing inferences about the effects of treatments and actions is a common challenge in
economics, epidemiology, and other fields. We adopt Rubin's potential outcomes framework …

Randomized trials typically estimate average relative treatment effects, but decisions on the
benefit of a treatment are possibly better informed by more individualized predictions of the …

The supplementary material contains details on treatment effect estimation performed
separately on RCT data (Section A) and on observational data (Section B), derivations of the …

The aim of many analyses of large databases is to draw causal inferences about the effects
of actions, treatments, or interventions. Examples include the effects of various options …

The main purpose of many medical studies is to estimate the effects of a treatment or
exposure on an outcome. However, it is not always possible to randomize the study …

We take steps toward causally interpretable meta-analysis by describing methods for
transporting causal inferences from a collection of randomized trials to a new target …