2011/08/01 by Stijn Vansteelandt, Jack Bowden, Manoochehr Babanezhad +1 · 117 citations
Mathematics · #Advanced Causal Inference Techniques #Artificial intelligence #Biology #Causal inference #Causal model #Computer science #Covariate #Econometrics #Economics #Estimation #Estimator #Inference #Instrumental variable #Logistic regression #Mathematics #Mendelian randomization #Odds #Odds ratio #Outcome (game theory) #Statistical Methods and Bayesian Inference #Statistical Methods and Inference #Statistics #stat.ME
paper · pdf · doi:10.1214/11-sts360
published in Statistical Science 26(3) (Institute of Mathematical Statistics) · Published in at http://dx.doi.org/10.1214/11-STS360 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org)
openalex publication_date 2011/08/01 · arxiv created 2012/01/12 · arxiv updated 2012/01/13 · openalex created_date 2020/11/23 · openalex updated_date 2026/08/06
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.