Applying Causal Inference Principles to the Analysis of Observational Studies in Physics Education

Numerous quantitative observational studies in the field of physics education research aim to determine the causal relationship between various student, classroom, and instructional factors. However, various errors can occur in estimating causal relationships among highly correlated variables, including those due to confounding, omitted variables, reverse causality, and selection. Many such errors can be understood in a unified way through a set of causal inference principles applied to causal network diagrams developed and growing in usage in other fields, such as medicine and the social sciences. Three fundamental causal structures – chain, fork, collider – and a set of rules for interpreting analyses under these structures can precisely model when these analytic errors are present. We examine how these analytic principles can detect potential errors when making causal predictions of the effects of potential interventions from observational studies.