The Causal Roadmap and Simulations to Improve the Rigor and Reproducibility of Real-data Applications.

ABSTRACT: The Causal Roadmap outlines a systematic approach to asking and answering questions of cause and effect: define the quantity of interest, evaluate needed assumptions, conduct statistical estimation, and carefully interpret results. To protect research integrity, it is essential that the algorithm for statistical estimation and inference be prespecified prior to conducting any effectiveness analyses. However, it is often unclear which algorithm will perform optimally for the real-data application. Instead, there is a temptation to simply implement one's favorite algorithm, recycling prior code or relying on the default settings of a computing package. Here, we call for the use of simulations that realistically reflect the application, including key characteristics such as strong confounding

SUBMITTER: Nance N 

PROVIDER: S-EPMC11444352 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature