Causal effects in longitudinal studies: Definition and maximum likelihood estimation

Recently, a nonparametric marginal structural model (NPMSM) approach to Causal Inference has been proposed [Neugebauer, R., van der Laan, M., 2006. Nonparametric causal effects based on marginal structural models. J. Statist. Plann. Inference (in press), 〈〈www http://www.sciencedirect.com/science/journal/03783758〉〉.] as an appealing practical alternative to the original parametric MSM (PMSM) approach introduced by Robins [Robins, J., 1998a. Marginal structural models. In: 1997 Proceedings of the American Statistical Association, American Statistical Association, Alexandria, VA, pp. 1–10]. The new MSM-based causal inference methodology generalizes the concept of causal effects: the proposed nonparametric causal effects are interpreted as summary measures of the causal effects defined with PMSMs. In addition, causal inference with NPMSM does not rely on the assumed correct specification of a parametric MSM but instead defines causal effects based on a user-specified working causal model which can be willingly misspecified. The NPMSM approach was developed for studies with point treatment data or with longitudinal data where the outcome is not time-dependent (typically collected at the end of data collection). In this paper, we generalize this approach to longitudinal studies where the outcome is time-dependent, i.e. collected throughout the span of the studies, and address the subsequent estimation inconsistency which could easily arise from a hasty generalization of the algorithm for maximum likelihood estimation. More generally, we provide an overview of the multiple causal effect representations which have been developed based on MSMs in longitudinal studies.