Drawing credible directed acyclic graphs for causal inference.

Area of Science:

• Causal inference
• Graphical models
• Epidemiology

Background:

• Causal directed acyclic graphs (DAGs) are crucial for causal inference, aiding in confounder identification and assessment of causal effect identification.
• Existing methodological work on DAGs often uses simplified examples, limiting their practical application for complex research scenarios.

Purpose of the Study:

• To introduce and demonstrate a stepwise, iterative procedure for constructing credible causal directed acyclic graphs (DAGs).
• To guide researchers in identifying confounding sources and integrating research design features, including quasi-experimental and randomized experiments.
• To address threats to validity such as measurement error and treatment noncompliance within the DAG construction process.

Main Methods:

• A stepwise, iterative procedure for drawing causal DAGs is presented.
• The method emphasizes identifying plausible sources of confounding.
• It incorporates features of experimental and quasi-experimental research designs and validity threats.

Main Results:

• The proposed procedure facilitates the construction of credible DAGs, which are sufficient for applied research.
• The iterative drawing process aids in systematically identifying and incorporating confounding variables.
• The method enhances the practical utility of DAGs in complex research contexts.

Conclusions:

• Developing a credible DAG, rather than a complete one, is adequate and achievable for applied causal inference.
• The presented iterative procedure offers a practical framework for building robust causal DAGs.
• This methodology supports researchers in navigating complex data-generating processes and improving causal effect estimation.