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Double robustness for complier parameters and a semi-parametric test for complier characteristics

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  • Rahul Singh
  • Liyang Sun

Abstract

SummaryWe propose a semi-parametric test to evaluate (a) whether different instruments induce subpopulations of compliers with the same observable characteristics, on average; and (b) whether compliers have observable characteristics that are the same as the full population, treated subpopulation, or untreated subpopulation, on average. The test is a flexible robustness check for the external validity of instruments. To justify the test, we characterise the doubly robust moment for Abadie’s class of complier parameters, and we analyse a machine learning update to weighting that we call the automaticweight. We use the test to reinterpret Angrist and Evans' different local average treatment effect estimates obtained using different instrumental variables.

Suggested Citation

  • Rahul Singh & Liyang Sun, 2024. "Double robustness for complier parameters and a semi-parametric test for complier characteristics," The Econometrics Journal, Royal Economic Society, vol. 27(1), pages 1-20.
    • Handle: RePEc:oup:emjrnl:v:27:y:2024:i:1:p:1-20.
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    File URL: http://hdl.handle.net/10.1093/ectj/utad019
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    Cited by:

    1. Mogstad, Magne & Torgovitsky, Alexander, 2024. "Instrumental variables with unobserved heterogeneity in treatment effects," Handbook of Labor Economics,, Elsevier.
    2. Manu Navjeevan & Rodrigo Pinto & Andres Santos, 2023. "Identification and Estimation in a Class of Potential Outcomes Models," Papers 2310.05311, arXiv.org.
    3. Tymon Słoczyński & S. Derya Uysal & Jeffrey M. Wooldridge, 2025. "Abadie’s Kappa and Weighting Estimators of the Local Average Treatment Effect," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 43(1), pages 164-177, January.
    4. David Bruns-Smith & Oliver Dukes & Avi Feller & Elizabeth L. Ogburn, 2023. "Augmented balancing weights as linear regression," Papers 2304.14545, arXiv.org, revised Jun 2024.

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