Fair comparisons: Life course selection bias and the effect of father absence on US children.

Studies have shown that father absence in opposite-gender couples has detrimental effects on children's wellbeing, net of selection bias. However, life course informed research suggests that the problem of selection bias may be more complex than currently thought. This paper shows the importance of nonparametrically adjusting for the trajectory of confounder covariates for the estimation of these effects. This paper uses data from the Fragile Families and Child Wellbeing Study to estimate the total effect of the departure of the biological father on children's wellbeing, as well as delayed or fade-out effects of this transition. The paper employs Bayesian additive regression trees, a machine learning and causal inference method suited for statistical models involving high-dimensional data sets. After adjusting for multiple time-invariant and -varying confounder covariates, as well as their history, estimates of father absence's effect on children's wellbeing are reduced substantially, a finding which may be referred to as life course selection bias. Results suggest early and middle childhood are not negatively affected by the departure of the biological father. Life course selection bias mostly affects estimates of this effect on adolescence, which is explained by children directly experiencing changes inparent's socioeconomic trajectories that lead to divorce or separation. This would not be the case when father absence is experienced in early childhood. Results suggest father absence is mostly a marker of life course cumulative socioeconomic disadvantage, not a cause of negative effects. • Life course selection bias: the trajectory of confounders may have a further bias causal effect estimates in demography. • Exposure of children to divorce is a marker of life course socioeconomic disadvantages, not a cause of negative effects. • When divorce or separation occur close to or during adolescence negative effects are found in the problem behavior domain. • Machine learning and causal inference can help in unbiasedly estimating the effects of complex family dynamics. [ABSTRACT FROM AUTHOR]