Testing Graphical Causal Models Using the R Package "dagitty". Issue 2 (16th February 2021)

• Record Type: Journal Article
• Authors: Ankan, Ankur; Wortel, Inge M. N.; Textor, Johannes
• Is Part Of: Current protocols. Volume 1:Issue 2(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-02-16
• Subjects: dagitty -- directed acyclic graphs (DAGs) -- independence testing -- model testing; Life sciences -- Laboratory manuals -- Periodicals; Biology -- Laboratory manuals -- Periodicals; Life sciences -- Technique -- Periodicals; Biology -- Technique -- Periodicals; 570.028
• DOI: 10.1002/cpz1.45
• Languages: English
• ISSNs: 2691-1299

Abstract

Abstract: Causal diagrams such as directed acyclic graphs (DAGs) are used in several scientific fields to help design and analyze studies that aim to infer causal effects from observational data; for example, DAGs can help identify suitable strategies to reduce confounding bias. However, DAGs can be difficult to design, and the validity of any DAG‐derived strategy hinges on the validity of the postulated DAG itself. Researchers should therefore check whether the assumptions encoded in the DAG are consistent with the data before proceeding with the analysis. Here, we explain how the R package 'dagitty', based on the web tool dagitty.net, can be used to test the statistical implications of the assumptions encoded in a given DAG. We hope that this will help researchers discover model specification errors, avoid erroneous conclusions, and build better models. © 2021 The Authors. Basic Protocol 1 : Constructing and importing DAG models from the dagitty web interface Support Protocol 1 : Installing R, RStudio, and the dagitty package Basic Protocol 2 : Testing DAGs against categorical data Basic Protocol 3 : Testing DAGs against continuous data Support Protocol 2 : Testing DAGs against continuous data with non‐linearities Basic Protocol 4 : Testing DAGs against a combination of categorical and continuous data