Doubly Robust Identification of Causal Effects of a Continuous Treatment using Discrete Instruments
arXiv (Cornell University)(2023)
摘要
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.
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关键词
causal effects,continuous treatment,robust
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