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Biometrika Advance Access published online on January 28, 2008
Biometrika, doi:10.1093/biomet/asm097
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© 2008 Biometrika Trust

Articles

Population intervention models in causal inference

Alan E. Hubbard and Mark J. van der Laan

Division of Biostatistics, University of California, Berkeley, California 94720, U.S.A. hubbard{at}stat.berkeley.edu laan{at}stat.berkeley.edu

Received for publication 1 November 2005. Revision received 1 June 2007.
  Abstract

We propose a new causal parameter, which is a natural extension of existing approaches to causal inference such as marginal structural models. Modelling approaches are proposed for the difference between a treatment-specific counterfactual population distribution and the actual population distribution of an outcome in the target population of interest. Relevant parameters describe the effect of a hypothetical intervention on such a population and therefore we refer to these models as population intervention models. We focus on intervention models estimating the effect of an intervention in terms of a difference and ratio of means, called risk difference and relative risk if the outcome is binary. We provide a class of inverse-probability-of-treatment-weighted and doubly-robust estimators of the causal parameters in these models. The finite-sample performance of these new estimators is explored in a simulation study.

Key Words: Attributable risk • Causal inference • Confounding • Counterfactual • Doubly-robust estimation • G-computation estimation • Inverse-probability-of-treatment-weighted estimation


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