Theoretical Considerations When Predicting the Outcome of Future Educational Interventions from Observational Studies

Recent developments in quantitative causal inference provide tools for modeling the mechanistic pathways through which interventions can impact students' educational experiences and outcomes. These tools can be applied when creating and interpreting causal network diagrams, where measured variables are nodes connected by directed links. Using these causal inference tools, we propose some key considerations that researchers should address when proposing educational interventions based on observational studies. These include: (i) considering multiple alternative models that make different causal predictions, (ii) specifying how interventions can plausibly impact specific nodes, and (iii) specifying how interventions can produce new mediations and moderations in a causal network. These considerations can be explicitly addressed by specifying the parameters of proposed future intervention studies and, later, comparing the results of those future studies to past observational results.