Journal
BIOMETRIKA
Volume 95, Issue 1, Pages 35-47Publisher
OXFORD UNIV PRESS
DOI: 10.1093/biomet/asm097
Keywords
attributable risk; causal inference; confounding; counterfactual; doubly-robust estimation; G-computation estimation; inverse-probability-of-treatment-weighted estimation
Funding
- NIEHS NIH HHS [R01 ES015493, R01 ES015493-02] Funding Source: Medline
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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.
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