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.

翻译：近期对物理教育研究（PER）的批评重新揭示了在无干预措施情况下从观测数据中得出因果推断时所面临的关键问题。为响应"因果推理入门"的呼吁，本文探讨了统计因果推断中的若干基础性问题。在梳理这些问题时，我们讨论了在其他领域已广泛应用的成熟因果推断方法，并探讨了它们在物理教育研究中的应用。通过使用模拟数据集，我们阐释了：（i）为何因果推断分析应控制混杂因子而避免控制中介变量与碰撞变量；（ii）多个拟议的因果模型可能均能与高度相关数据集拟合。最后，我们探讨了如何利用这些因果推断方法来表征和解释定量物理教育研究中现存的问题。本文将始终围绕一个核心议题：基于观测研究的定量结果无法支持研究者提出的因果模型优于其他备选模型。针对这一问题，我们提出物理教育研究中开展统计因果推断的观测研究所应扮演的明确角色：提出未来的干预研究方案并预测其成效。类比物理学中理论驱动实验的普遍关联，物理教育研究中的观测研究可对干预措施的因果效应作出定量预测，而未来的干预研究则能直接检验这些预测。