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When engagement with a randomized trial is driven by factors that affect theoutcome or when trial engagement directly affects the outcome independent oftreatment, the average treatment effect among trial participants is unlikely togeneralize to a target population. In this paper, we use counterfactual andgraphical causal models to examine under what conditions we can generalizecausal inferences from a randomized trial to the target population oftrial-eligible individuals. We offer an interpretation of generalizabilityanalyses using the notion of a hypothetical intervention to "scale-up" trialengagement to the target population. We consider the interpretation ofgeneralizability analyses when trial engagement does or does not directlyaffect the outcome, highlight connections with censoring in longitudinalstudies, and discuss identification of the distribution of counterfactualoutcomes via g-formula computation and inverse probability weighting. Last, weshow how the methods can be extended to address time-varying treatments,non-adherence, and censoring.

Generalizing causal inferences from randomized trials: counterfactual and graphical identification