title: Estimating controlled direct effects of restrictivefeeding practices in the 'Early dieting in girls' study.
abstract: In this article, we examine the causal effect of parental restrictive feeding practices on children's weight status. An important mediator is children's self-regulation status. Recent approaches interpret mediation effects based on the potential outcomes framework. Inverse probability weighting based on propensity scores are used to adjust for confounding and reduce the dimensionality of confounders simultaneously. We show that combining machine learning algorithms and logistic regression to estimate the propensity scores can be more accurate and efficient in estimating the controlled direct effects than using logistic regression alone. A data application shows that the causal effect of mother's restrictive feeding differs according to whether the daughter eats in the absence of hunger.
authors.affiliation: Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Canada.
authors.forename: Yeying
authors.initials: Y
authors.lastname: Zhu
authors.affiliation: Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, USA.
authors.forename: Debashis
authors.initials: D
authors.lastname: Ghosh
authors.affiliation: The Methodology Center, Pennsylvania State University, University Park, USA.
authors.forename: Donna L
authors.initials: DL
authors.lastname: Coffman
authors.affiliation: The Center for Childhood Obesity Research, Pennsylvania State University, University Park, USA.
authors.forename: Jennifer S
authors.initials: JS
authors.lastname: Savage
keywords: Causal inference; Controlled direct effects; Generalized boosted model; Mediator; Model combining; Random forests