Marginal structural models (MSMs) are causal models designed to adjust for time-dependent confounding in observational studies of time-varying treatments. MSMs are powerful tools for assessing causality with complicated, longitudinal data sets but have not been widely used by practitioners. The objective of this paper is to illustrate the fitting of an MSM for the causal effect of iron suppleme...

Drawing inferences about the effects of treatments and actions is a common challenge in economics, epidemiology, and other fields. We adopt Rubin’s potential outcomes framework for causal inference and propose two methods serving complementary purposes. One can be used to estimate average causal effects, assuming no confounding given measured covariates. The other can be used to assess how the ...

Little is known about the effects of placing children who are abused or neglected into foster care. This paper uses the placement tendency of child protection investigators as an instrumental variable to identify causal effects of foster care on long-term outcomes--including juvenile delinquency, teen motherhood, and employment--among children in Illinois where a rotational assignment process e...

Abstract This study proposes an econometric framework to interpret and empirically decompose the difference between IV OLS estimates given by a linear regression model when true causal effects of treatment are nonlinear in levels heterogeneous across covariates. I show that IV–OLS coefficient gap consists three estimable components: weights on covariates, levels, identified marginal arises from...

In this article, we show the general relation between standardization methods and marginal structural models. Standardization has been recognized as a method to control confounding and to estimate causal parameters of interest. Because standardization requires stratification by confounders, the sparse-data problem will occur when stratified by many confounders and one then might have an unstabl...

In this paper, we considered a missing outcome problem in causal inferences for a randomized encouragement design study. We proposed both moment and maximum likelihood estimators for the marginal distributions of potential outcomes and the local complier average causal effect (CACE) parameter. We illustrated our methods in a randomized encouragement design study on the effectiveness of flu shots.

Of widespread interest in social science are observational studies in which entities (persons, schools, states, countries, etc.) are exposed to varied treatment conditions over time. As in all observational studies, the non-randomized assignment of treatments poses challenges to valid causal inference. An attractive feature of panel studies with time-varying treatments, however, is that the des...