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Marginal Structural Models versus Structural nested Models as Tools for Causal inference

  • James M. Robins
Part of the The IMA Volumes in Mathematics and its Applications book series (IMA, volume 116)

Abstract

Robins (1993, 1994, 1997, 1998ab) has developed a set of causal or counterfactual models, the structural nested models (SNMs). This paper describes an alternative new class of causal models — the (non-nested) marginal structural models (MSMs). We will then describe a class of semiparametric estimators for the parameters of these new models under a sequential randomization (i.e., ignorability) assumption. We then compare the strengths and weaknesses of MSMs versus SNMs for causal inference from complex longitudinal data with time-dependent treatments and confounders. Our results provide an extension to continuous treatments of propensity score estimators of an average treatment effect.

Keywords

Efficient Score Failure Time Causal Inference Semiparametric Model Marginal Structural Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 2000

Authors and Affiliations

  • James M. Robins
    • 1
  1. 1.Epidemiology and BiostatisticsHarvard School of Public HealthBostonUSA

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