A parametric marginal structural model (PMSM) approach to Causal Inference has been favored since the introduction of MSMs by Robins [1998a. Marginal structural models. In: 1997 Proceedings of the American Statistical Association. American Statistical Association, Alexandria, VA, pp. 1-10]. We propose an alternative, nonparametric MSM (NPMSM) approach that extends the definition of causal parameters of interest and causal effects. This approach is appealing in practice as it does not require correct specification of a parametric model but instead relies on a working model which can be willingly misspecified. We propose a methodology for longitudinal data to generate and estimate so-called NPMSM parameters describing so-called nonparametric causal effects and provide insight on how to interpret these parameters causally in practice. Results are illustrated with a point treatment simulation study. The proposed NPMSM approach to Causal Inference is compared to the more typical PMSM approach and we contribute to the general understanding of PMSM estimation by addressing the issue of PMSM misspecification. © 2006 Elsevier B.V. All rights reserved.
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