Causality and ability beliefs: An introduction to confounders and colliders
Causality and ability beliefs: An introduction to confounders and colliders

Summary/Abstract: Causal inference is a fundamental goal of many research endeavors, including scholarship in the field of language education and learning. Randomized controlled trialsare considered an ideal design to test causal claims, but not all claims can be sub-jected to experimental treatment due to ethical and practical constraints. In this ar-ticle, we provide an overview of the conditions under which causal inference maybe made from observational data. This includes recognition of the role of confound-ers and colliders; the former are common causes of the independent and depend-ent variables and must be controlled, while the latter are common effects and mustnot be controlled. We illustrate these ideas with two examples involving ability be-liefs and demonstrate them through directed acyclic graphs. We discuss the impli-cations of this approach to causal inference from observational data, specifically inindividual differences in language learning research, highlighting the need for ex-plicit modeling of causal relationships and the risk of the atheoretical inclusion ofvariables, whether as controls, predictors, or covariates

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