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.

SUBMITTER: Chan GCK 

PROVIDER: S-EPMC9545953 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature