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Doubly Robust Identification of Causal Effects of a Continuous Treatment using Discrete Instruments

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  • Yingying Dong
  • Ying-Ying Lee

Abstract

Many empirical applications estimate causal effects of a continuous endogenous variable (treatment) using a binary instrument. Estimation is typically done through linear 2SLS. This approach requires a mean treatment change and causal interpretation requires the LATE-type monotonicity in the first stage. An alternative approach is to explore distributional changes in the treatment, where the first-stage restriction is treatment rank similarity. We propose causal estimands that are doubly robust in that they are valid under either of these two restrictions. We apply the doubly robust estimation to estimate the impacts of sleep on well-being. Our new estimates corroborate the usual 2SLS estimates.

Suggested Citation

  • Yingying Dong & Ying-Ying Lee, 2023. "Doubly Robust Identification of Causal Effects of a Continuous Treatment using Discrete Instruments," Papers 2310.18504, arXiv.org, revised Feb 2024.
    • Handle: RePEc:arx:papers:2310.18504
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    References listed on IDEAS

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    1. Joshua Angrist & Victor Chernozhukov & Iván Fernández-Val, 2006. "Quantile Regression under Misspecification, with an Application to the U.S. Wage Structure," Econometrica, Econometric Society, vol. 74(2), pages 539-563, March.
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