A0814
Title: Nonlinear and nonseparable structural functions in fuzzy regression discontinuity designs
Authors: Haitian Xie - UC San Diego (United States) [presenting]
Abstract: The aim is to examine 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 semiparametric 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.
