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 on the basis of the potential outcomes framework. Inverse probability weighting based on propensity scores are used to adjust for confounding and to 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.
|Original language||English (US)|
|Number of pages||16|
|Journal||Journal of the Royal Statistical Society. Series C: Applied Statistics|
|State||Published - Jan 1 2016|
All Science Journal Classification (ASJC) codes
- Statistics and Probability
- Statistics, Probability and Uncertainty