A longitudinal causal graph analysis investigating modifiable risk factors and obesity in a European cohort of children and adolescents

A Cohort Causal Graph (CCG) over the life-course from childhood to adolescence is estimated to identify potential causes of obesity and to determine promising targets for prevention strategies. We adapt a popular causal discovery algorithm to deal with missing values by multiple imputation and with temporal cohort structure. To estimate possible causal effects of modifiable risk factors at baseline on obesity six years later, we used the "Intervention-calculus when the Directed Acyclic Graph is Absent" and double machine learning with confounder adjustment based on the obtained CCG. Causal relations among 51 variables were analysed including obesity, early life factors, lifestyle and cultural background of 5,112 children from the European IDEFICS/I.Family cohort across three waves (2007-2014). The resulting CCG shows some but not many and only indirect possible pathways from earlier modifiable risk factors such as audio-visual media consumption (AVM) to later obesity. The estimated causal effects suggested that promising interventions would encourage longer sleep or reduce AVM during childhood, both slightly decreasing expected body mass index six years later. But overall, no or only weak causal effects could be found for hypothetical interventions on individual behaviors in early childhood on later obesity.