Joseph Schafer () (Penn State University) Joseph Kang (Penn State University)
Abstract
Literature on causal inference has emphasized the average causal effect, defined as the mean difference in potential outcomes under different treatment conditions. We consider marginal regression models that describe how causal effects vary in relation to covariates. To estimate parameters, we replace missing potential outcomes in estimating functions with fitted values from imputation models that include confounders and prognostic variables as predictors. When the imputation and analytic models are linear, our procedure is equivalent to maximum likelihood for normally distributed outcomes and covariates. Robustness to misspecification of the imputation models is enhanced by including functions of propensity scores as regressors. In simulations where the analytic, imputation, and propensity models are misspecified, the method performs better than inverse-propensity weighting. Using data from the National Longitudinal Study of Adolescent Health, we analyze the effects of dieting on emotional distress in the population of girls who diet, taking into account the study's complex sample design.
Download Info
To our knowledge, this item is not available for
download. To find whether it is available, there are three
options:
1. Check below under "Related research" whether another version of this item is available online.
2. Check on the provider's web page
whether it is in fact available.
3. Perform a search for a similarly titled item that would be
available.
Did you know? You can create a compilation of all publications of a group of people, say alumni of a program, your students or memers of an association.