Statistical Science

On Instrumental Variables Estimation of Causal Odds Ratios

Stijn Vansteelandt, Jack Bowden, Manoochehr Babanezhad, and Els Goetghebeur

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Inference for causal effects can benefit from the availability of an instrumental variable (IV) which, by definition, is associated with the given exposure, but not with the outcome of interest other than through a causal exposure effect. Estimation methods for instrumental variables are now well established for continuous outcomes, but much less so for dichotomous outcomes. In this article we review IV estimation of so-called conditional causal odds ratios which express the effect of an arbitrary exposure on a dichotomous outcome conditional on the exposure level, instrumental variable and measured covariates. In addition, we propose IV estimators of so-called marginal causal odds ratios which express the effect of an arbitrary exposure on a dichotomous outcome at the population level, and are therefore of greater public health relevance. We explore interconnections between the different estimators and support the results with extensive simulation studies and three applications.

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Statist. Sci., Volume 26, Number 3 (2011), 403-422.

First available in Project Euclid: 31 October 2011

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Causal effect causal odds ratio instrumental variable marginal effect Mendelian randomization logistic structural mean model


Vansteelandt, Stijn; Bowden, Jack; Babanezhad, Manoochehr; Goetghebeur, Els. On Instrumental Variables Estimation of Causal Odds Ratios. Statist. Sci. 26 (2011), no. 3, 403--422. doi:10.1214/11-STS360.

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