Utilizing causal diagrams across quasi-experimental approaches

Recent developments in computer science have substantially advanced the use of observational causal inference under Pearl's structural causal model (SCM) framework. A key tool in the application of SCM is the use of casual diagrams, used to visualize the causal structure of a system or process under study. Here, we show how causal diagrams can be extended to ensure proper study design under quasi-experimental settings, including propensity score analysis, before-after-control-impact studies, regression discontinuity design, and instrumental variables. Causal diagrams represent a unified approach to variable selection across methodologies and should be routinely applied in ecology research with causal implications.

Automated summary

Recent developments in computer science have advanced the use of causal inference and causal diagrams in observational studies, providing a unified approach to variable selection across different methodologies. This paper demonstrates how causal diagrams can be extended to ensure proper study design under quasi-experimental settings and highlights the importance of routinely applying causal diagrams in ecology research.