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We consider the challenges associated with causal inference in settings wheredata from a randomized trial is augmented with control data from an externalsource to improve efficiency in estimating the average treatment effect (ATE).Through the development of a formal causal inference framework, we outlinesufficient causal assumptions about the exchangeability between the internaland external controls to identify the ATE and establish the connection to anovel graphical criteria. We propose estimators, review efficiency bounds,develop an approach for efficient doubly-robust estimation even when unknownnuisance models are estimated with flexible machine learning methods, anddemonstrate finite-sample performance through a simulation study. To illustratethe ideas and methods, we apply the framework to a trial investigating theeffect of risdisplam on motor function in patients with spinal muscular atrophyfor which there exists an external set of control patients from a previoustrial.

A Causal Inference Framework for Leveraging External Controls in Hybrid Trials