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” in PER, this paper discusses some of the fundamental issues in 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 dataset. 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 in observational studies: A good quantitative model fit for a proposed causal model is not sufficient to support that proposed model over 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 the way that theory can motivate experiments in physics, observational studies in PER can predict the causal effects of interventions, and future intervention studies can test those predictions directly.

中文翻译：

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观察物理教育研究统计因果推断的关键问题

最近对物理教育研究（PER）研究的批评已经阐明了从不存在干预的观察数据中得出因果推论的关键问题。为了响应 PER 中“因果推理入门”的号召，本文讨论了统计因果推理中的一些基本问题。在回顾这些问题时，我们讨论了其他领域中常用的行之有效的因果推理方法，并讨论了它们在 PER 中的应用。使用模拟数据集，我们说明了（i）为什么因果推理分析应该控制混杂因素，而不是控制中介者和碰撞者，以及（ii）多个提出的因果模型可以适合高度相关的数据集。最后，我们讨论如何使用这些因果推理方法来表示和解释定量 PER 中的现有问题。在整个过程中，我们讨论了观察研究中的一个中心问题：适合所提出的因果模型的良好定量模型不足以支持该所提出的模型优于替代模型。为了解决这个问题，我们提出观察性研究在 PER 中的明确作用，以得出统计因果推论：提出未来的干预研究并预测其结果。 PER 的观察性研究可以预测干预措施的因果效应，而未来的干预研究可以直接检验这些预测，这反映了理论激发物理学实验的方式。