Causal inference with observational data in addiction research

Abstract

Randomized controlled trials (RCTs) are the gold standard for making causal inferences, but RCTs are often not feasible in addiction research for ethical and logistic reasons. Observational data from real-world settings have been increasingly used to guide clinical decisions and public health policies. This paper introduces the potential outcomes framework for causal inference and summarizes well-established causal analysis methods for observational data, including matching, inverse probability treatment weighting, the instrumental variable method and interrupted time-series analysis with controls. It provides examples in addiction research and guidance and analysis codes for conducting these analyses with example data sets.