Methods, understandings, and expressions of causality in educational research

The opening pages of Pearl and Mackenzie’s volume The Book of Why: The New Science of Cause and Effect (2018) herald their captivating romp through causality by referring to a “ladder of causation” (p. 28) that starts from association (by seeing and observing), moves up to intervention (by doing and intervening), and thence to counterfactuals (by imagining, retrospection, and understanding). Each rung of the ladder establishes causality more certainly. Humans think causally. Causality can be studied by many methods. Here, Pearl and Mackenzie (2018) state that statistical analysis does not simply concern data and their methods of analysis; rather, there is a need for an “understanding of the process that produces the data” (p. 85). Such “understanding” comes from introducing causality, as causality yields something additional to the original data. “Methods” of data analysis are informed by an “understanding” of causality, as this Editorial shows. Pearl and Mackenzie write that if we remove the understanding of causation from statistical analysis, all that we are left with is data reduction, which does not tell us much. The papers in this issue move forward from “methods” to “understanding” data with regard to causality. Further, the Editorial indicates how easily it is to find expressions of causality in articles; this should caution researchers to take care in the wording that they use. The Editorial below draws attention to wording in deliberately italicising causal words in quoting from the articles in this issue. For example, is causality really being demonstrated, or, like Pearl and MacKenzie’s lowest rung of the ladder, is there merely association? Causality, be it post hoc or ante hoc, is self-evidently important in education. However, how we adduce causality is far from straightforward, and the papers in this issue yield insights into, and cautions concerning, claims for, and demonstrations of, causality. The papers here indicate methods, challenges, outcomes, and benefits of studying causality. The challenges of “methods” and “understanding” when investigating causality are legion. Witness, for example, in the perennial search for causality, its differences from association, prediction, explanation, inference, influence, correlation, accounting for, correspondence to, purposiveness, and a whole armoury of other words. Look at the dangers of working with mediating, confounding, and moderating variables, transitivity, or controlling out almost everything such that what remains is very little. Wrestle with underdetermination, overdetermination, supervenience, and the difficulties of mereology. Consider the challenges of probabilistic causation and Bayesian approaches, leavened by multilevel causal modelling. Add to these the context-dense, variable-rich, causally complex world of education, and the attraction of Pearl and MacKenzie’s (2018) “childlike simplicity” (p. 39) of a causal diagram evaporates in front of our eyes. Little wonder it is, then, that authors studying causality in the field of education choose their words very carefully. This helps them to avoid charges of assumed or