So many variables, but what causes what?

Paradis’ keynote article provides a comprehensive overview of factors influencing bilingual children’s dual language abilities. It includes the ‘usual suspects’, such as input quantity, and also highlights areas requiring further investigation, such as cognitive abilities. As such, it will no doubt serve as a valuable basis for the field as we move forward. Paradis quite rightly points out that whilst some of these factors may be unidirectionally related to language abilities, suggesting causality, for many others such relations are bi- or multidirectional and as such, caution is required in interpreting them. In order to pinpoint the nature and direction of these relations (currently absent from Figure 1 in the keynote), more complex analytic techniques are needed, as Paradis herself notes: “The relations among attitudes/identity, input and interaction, and perhaps social adjustment and wellbeing, are likely to be complex; therefore, more complex analytic techniques are needed to understand the path(s) between family attitudes about the HL on one hand, and children’s HL outcomes on the other.” (Paradis, 2023: 19). In this commentary, we provide an illustration of how the complex relationships between the variables discussed in Paradis’s keynote article could be conceptualised within a causal inference approach. We offer a modest starting point by summarising key features of causal inference modelling and by illustrating how it might help us better understand what causes what.