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Title: Nonlinear and Nonseparable Structural Functions in Fuzzy Regression Discontinuity Designs

Citation Type: Miscellaneous

Publication Year: 2022

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Abstract: This paper examines the identification and estimation of the structural function in fuzzy regression discontinuity (RD) designs with a continuous treatment variable. Under a dual monotonicity condition, we show that the nonlinear and nonseparable structural function can be nonparametrically identified at the RD cutoff. The dual monotonicity condition requires that the structural function and the treatment choice be strictly increasing in the unobserved causal factor. This condition is satisfied by standard parametric models used in practice. The identification result contrasts with the local average treatment effect literature, where only a certain weighted average of the structural function is identified. We propose a three-step semiparametric estimation procedure and derive the asymptotic distribution of the estimator. The semi-parametric estimator achieves the same convergence rate as in the case of a binary treatment variable. As an application of the method, we estimate the causal effect of sleep time on health status by the discontinuity in natural light timing at time-zone boundaries.

Url: https://arxiv.org/pdf/2204.08168.pdf

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Authors: Xie, Haitian

Publisher: University of California, San Diego

Data Collections: IPUMS CPS, IPUMS Time Use - ATUS

Topics: Population Data Science

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