Model selection isn't causal inference

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.