Generalizing causal inferences from randomized trials: counterfactual and graphical identification

TL;DR

This paper explores conditions under which causal inferences from randomized trials can be generalized to broader populations using counterfactual and graphical causal models, addressing issues like trial engagement effects and time-varying treatments.

Contribution

It introduces a framework for generalizing causal inferences using counterfactual and graphical models, including methods for addressing trial engagement and time-varying treatments.

Findings

Conditions for generalizability using causal models

Interpretation of generalizability via hypothetical interventions

Extensions to handle time-varying treatments and non-adherence

Abstract

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 treatment effect among trial participants is unlikely to generalize to a target population. In this paper, we use counterfactual and graphical causal models to examine under what conditions we can generalize causal inferences from a randomized trial to the target population of trial-eligible individuals. We offer an interpretation of generalizability analyses using the notion of a hypothetical intervention to "scale-up" trial engagement to the target population. We consider the interpretation of generalizability analyses when trial engagement does or does not directly affect the outcome, highlight connections with censoring in longitudinal studies, and discuss identification of the distribution of counterfactual outcomes via g-formula computation and inverse probability weighting. Last, we show how the methods can be extended to address time-varying treatments, non-adherence, and censoring.