Statistical causal inference methods for observational research in PER: a primer

Abstract

Recent critiques of Physics Education Research (PER) studies have revoiced the critical issues when drawing causal inferences from observational data where no intervention is present. In response to a call for a "causal reasoning primer", this paper discusses some of the fundamental issues underlying statistical causal inference. In reviewing these issues, we discuss well-established causal inference methods commonly applied in other fields and discuss their application to PER. Using simulated data sets, we illustrate (i) why analysis for causal inference should control for confounders but not control for mediators and colliders and (ii) that multiple proposed causal models can fit a highly correlated data set. Finally, we discuss how these causal inference methods can be used to represent and explain existing issues in quantitative PER. Throughout, we discuss a central issue: quantitative results from observational studies cannot support a researcher's proposed causal model over other alternative models. To address this issue, we propose an explicit role for observational studies in PER that draw statistical causal inferences: proposing future intervention studies and predicting their outcomes. Mirroring a broader connection between theoretical motivating experiments in physics, observational studies in PER can make quantitative predictions of the causal effects of interventions, and future intervention studies can test those predictions directly.