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On falsification of the binary instrumental variable model

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  • Linbo Wang
  • James M. Robins
  • Thomas S. Richardson

Abstract

SUMMARY Instrumental variables are widely used for estimating causal effects in the presence of unmeasured confounding. The discrete instrumental variable model has testable implications for the law of the observed data. However, current assessments of instrumental validity are typically based solely on subject-matter arguments rather than these testable implications, partly due to a lack of formal statistical tests with known properties. In this paper, we develop simple procedures for testing the binary instrumental variable model. Our methods are based on existing techniques for comparing two treatments, such as the $t$-test and the Gail–Simon test. We illustrate the importance of testing the instrumental variable model by evaluating the exogeneity of college proximity using the National Longitudinal Survey of Young Men.

Suggested Citation

  • Linbo Wang & James M. Robins & Thomas S. Richardson, 2017. "On falsification of the binary instrumental variable model," Biometrika, Biometrika Trust, vol. 104(1), pages 229-236.
    • Handle: RePEc:oup:biomet:v:104:y:2017:i:1:p:229-236.
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    File URL: http://hdl.handle.net/10.1093/biomet/asw064
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    References listed on IDEAS

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    1. Minsu Chang & Sokbae Lee & Yoon‐Jae Whang, 2015. "Nonparametric tests of conditional treatment effects with an application to single‐sex schooling on academic achievements," Econometrics Journal, Royal Economic Society, vol. 18(3), pages 307-346, October.
    2. Martin Huber & Giovanni Mellace, 2015. "Testing Instrument Validity for LATE Identification Based on Inequality Moment Constraints," The Review of Economics and Statistics, MIT Press, vol. 97(2), pages 398-411, May.
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    Cited by:

    1. Xuemei Fan & Ziyue Nan & Yuanhang Ma & Yingdan Zhang & Fei Han, 2021. "Research on the Spatio-Temporal Impacts of Environmental Factors on the Fresh Agricultural Product Supply Chain and the Spatial Differentiation Issue—An Empirical Research on 31 Chinese Provinces," IJERPH, MDPI, vol. 18(22), pages 1-26, November.
    2. Myoung‐jae Lee, 2021. "Instrument residual estimator for any response variable with endogenous binary treatment," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(3), pages 612-635, July.

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