Causal Inference Methods for Combining Randomized Trials and Observational Studies: A Review.

ABSTRACT: With increasing data availability, causal effects can be evaluated across different data sets, both randomized controlled trials (RCTs) and observational studies. RCTs isolate the effect of the treatment from that of unwanted (confounding) co-occurring effects but they may suffer from unrepresentativeness, and thus lack external validity. On the other hand, large observational samples are often more representative of the target population but can conflate confounding effects with the treatment of interest. In this paper, we review the growing literature on methods for causal inference on combined RCTs and observational studies, striving for the best of both worlds. We first discuss identification and estimation methods that improve generalizability of RCTs using the representativeness of o

SUBMITTER: Colnet B 

PROVIDER: S-EPMC12499922 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature