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Instrumental Variable Estimators for Binary Outcomes

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  • Paul Clarke
  • Frank Windmeijer

Abstract

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.

Suggested Citation

  • Paul Clarke & Frank Windmeijer, 2010. "Instrumental Variable Estimators for Binary Outcomes," The Centre for Market and Public Organisation 10/239, The Centre for Market and Public Organisation, University of Bristol, UK.
    • Handle: RePEc:bri:cmpowp:10/239
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    References listed on IDEAS

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    More about this item

    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;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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