Abstract
Ecologists often rely on observational data to understand causal
relationships. Although observational causal inference methodologies
exist, model selection based on information criterion (e.g., AIC)
remains a common approach used to understand ecological relationships.
However, such approaches are meant for predictive inference and is not
appropriate for drawing causal conclusions. Here, we highlight the
distinction between predictive and causal inference and show how model
selection techniques can lead to biased causal estimates. Instead, we
encourage ecologists to apply the backdoor criterion, a graphical rule
that can be used to determine causal relationships across observational
studies.