ON USING LINEAR QUANTILE REGRESSIONS FOR CAUSAL INFERENCE
证明线性分位数回归的斜率参数衡量了条件分位数函数局部斜率的加权平均，并进一步表明该参数衡量了一般结构函数偏效应的加权平均，支持其在存在非线性和多元未观测异质性时用于因果推断。
We show that the slope parameter of the linear quantile regression measures a weighted average of the local slopes of the conditional quantile function. Extending this result, we also show that the slope parameter measures a weighted average of the partial effects for a general structural function. Our results support the use of linear quantile regressions for causal inference in the presence of nonlinearity and multivariate unobserved heterogeneity. The same conclusion applies to linear regressions.