Identification of Marginal Effects in Nonseparable Models Without Monotonicity
证明，在不可分模型中即使不假设单调性，分位数也能识别局部平均结构导数，对使用联合分布进行因果推断的经济学研究有参考价值。
Nonseparable models do not impose any type of additivity between the unobserved part and the observable regressors, and are therefore ideal for many economic applications. To identify these models using the entire joint distribution of the data as summarized in regression quantiles, monotonicity in unobservables has frequently been assumed. This paper establishes that in the absence of monotonicity, the quantiles identify local average structural derivatives of nonseparable models. Copyright The Econometric Society 2007.