title: Population intervention models in causal inference.
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.
authors.affiliation: Division of Biostatistics, University of California, Berkeley, California 94720, U.S.A.  hubbard@stat.berkeley.edu laan@stat.berkeley.edu.
authors.forename: Alan E
authors.initials: AE
authors.lastname: Hubbard
authors.forename: Mark J VAN DER
authors.initials: MJ
authors.lastname: Laan