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Strengthening Instrumental Variables Through Weighting


  • Douglas Lehmann

    () (University of Michigan)

  • Yun Li

    (University of Michigan)

  • Rajiv Saran

    (University of Michigan)

  • Yi Li

    (University of Michigan)


Abstract Instrumental variable (IV) methods are widely used to deal with the issue of unmeasured confounding and are becoming popular in health and medical research. IV models are able to obtain consistent estimates in the presence of unmeasured confounding, but rely on assumptions that are hard to verify and often criticized. An instrument is a variable that influences or encourages individuals toward a particular treatment without directly affecting the outcome. Estimates obtained using instruments with a weak influence over the treatment are known to have larger small-sample bias and to be less robust to the critical IV assumption that the instrument is randomly assigned. In this work, we propose a weighting procedure for strengthening the instrument while matching. Through simulations, weighting is shown to strengthen the instrument and improve robustness of resulting estimates. Unlike existing methods, weighting is shown to increase instrument strength without compromising match quality. We illustrate the method in a study comparing mortality between kidney dialysis patients receiving hemodialysis or peritoneal dialysis as treatment for end-stage renal disease.

Suggested Citation

  • Douglas Lehmann & Yun Li & Rajiv Saran & Yi Li, 2017. "Strengthening Instrumental Variables Through Weighting," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 9(2), pages 320-338, December.
  • Handle: RePEc:spr:stabio:v:9:y:2017:i:2:d:10.1007_s12561-016-9149-9
    DOI: 10.1007/s12561-016-9149-9

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    References listed on IDEAS

    1. Lu, Bo & Greevy, Robert & Xu, Xinyi & Beck, Cole, 2011. "Optimal Nonbipartite Matching and Its Statistical Applications," The American Statistician, American Statistical Association, vol. 65(1), pages 21-30.
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    5. Douglas Staiger & James H. Stock, 1997. "Instrumental Variables Regression with Weak Instruments," Econometrica, Econometric Society, vol. 65(3), pages 557-586, May.
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    7. Brookhart M. Alan & Schneeweiss Sebastian, 2007. "Preference-Based Instrumental Variable Methods for the Estimation of Treatment Effects: Assessing Validity and Interpreting Results," The International Journal of Biostatistics, De Gruyter, vol. 3(1), pages 1-25, December.
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