Instrumental Variable Estimators for Binary Outcomes
Paul Clarke () and
Frank Windmeijer ()
The Centre for Market and Public Organisation from Department of Economics, University of Bristol, UK
Instrumental variables (IVs) can be used to construct estimators of exposure effects on the outcomes of studies affected by non-ignorable selection of the exposure. Estimators which fail to adjust for the effects of non-ignorable selection will be biased and inconsistent. Such situations commonly arise in observational studies, but even randomised controlled trials can be affected by non-ignorable participant non-compliance. In this paper, we review IV estimators for studies in which the outcome is binary. Recent work on identification is interpreted using an integrated structural modelling and potential outcomes framework, within which we consider the links between different approaches developed in statistics and econometrics. The implicit assumptions required for bounding causal effects and point-identification by each estimator are highlighted and compared within our framework. Finally, the implications for practice are discussed.
Keywords: bounds; causal inference; generalized method of moments; local average treatment effects; marginal structural models; non-compliance; parameter identification; potential outcomes; structural mean models; structural models (search for similar items in EconPapers)
JEL-codes: C13 C14 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm
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Journal Article: Instrumental Variable Estimators for Binary Outcomes (2012)
Working Paper: Instrumental Variable Estimators for Binary Outcomes (2009)
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Persistent link: https://EconPapers.repec.org/RePEc:bri:cmpowp:10/239
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