The Target Trial Framework for Causal Inference From Observational Data: Why and When Is It Helpful?

Abstract

When randomized trials are not available to answer a causal question about the comparative effectiveness or safety of interventions, causal inferences are drawn using observational data. A helpful 2-step framework for causal inference from observational data is 1) specifying the protocol of the hypothetical randomized pragmatic trial that would answer the causal question of interest (the target trial), and 2) using the observational data to attempt to emulate that trial. The target trial framework can improve the quality of observational analyses by preventing some common biases. In this article, we discuss the utility and scope of applications of the framework. We clarify that target trial emulation resolves problems related to incorrect design but not those related to data limitations. We also describe some settings in which adopting this approach is advantageous to generate effect estimates that can close the gaps that randomized trials have not filled. In these settings, the target trial framework helps reduce the ambiguity of causal questions.