具有内生暴露和轻尾误差的因果效应识别与推断
Causal Effect Identification and Inference with Endogenous Exposures and a Light-tailed Error
AI总结：
针对内生性导致的因果推断挑战，本文提出基于轻尾误差假设的极端条件分位数识别方法，无需额外参数假设或辅助变量，结合极端分位数回归估计因果效应，经模拟和汽车销售数据分析验证有效性。
摘要：
内生性在各研究领域的因果推断中构成重大挑战。本文提出一种新方法，用于在存在内生性时识别和估计因果效应。我们考虑含内生暴露和加性误差项的结构方程，假设误差项具有轻尾性，证明可通过对比给定暴露下结果的极端条件分位数来识别因果效应。与现有多数结果不同，我们的识别方法不依赖额外参数假设或辅助变量。基于该识别结果，我们开发新方法，用极端分位数回归估计因果效应；在一般加性结构方程下建立所提极端估计量的一致性，并在线性模型设定中证明其渐近正态性。对汽车销售数据集的模拟与数据分析表明，该方法在处理内生性方面有效。
英文摘要：
Endogeneity poses significant challenges in causal inference across various research domains. This paper proposes a novel approach to identify and estimate causal effects in the presence of endogeneity. We consider a structural equation with endogenous exposures and an additive error term. Assuming the light-tailedness of the error term, we show that the causal effect can be identified by contrasting extreme conditional quantiles of the outcome given the exposures. Unlike many existing results, our identification approach does not rely on additional parametric assumptions or auxiliary variables. Building on the identification result, we develop a new method that estimates the causal effect using extreme quantile regression. We establish the consistency of the proposed extreme-based estimator under a general additive structural equation and demonstrate its asymptotic normality in the linear model setting. Simulations and data analysis of an automobile sale dataset show the effectiveness of our method in handling endogeneity.
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