协变量偏移下外部有效性的简单鲁棒性度量
A Simple Measure of Robustness for External Validity under Covariate Shifts
AI总结：
本文提出一种新的鲁棒性度量δ*，通过去偏GMM估计在协变量分布变化下评估政策效应的外部有效性，并利用基准测试和校准解释其大小。
摘要：
本文研究了估计的政策效应对协变量分布变化的鲁棒性，这是（准）实验结果外部有效性的关键决定因素。我提出了一种新的鲁棒性度量δ*，它衡量使关于政策效应的经验主张（例如，ATE > 0）无效所需的最小协变量偏移。我通过去偏GMM估计δ*，实现了参数收敛速度，同时适应了处理效应异质性的机器学习估计器（例如，LASSO、随机森林、神经网络）。我开发了基准测试和校准练习来解释δ*的大小。我在俄勒冈健康保险实验的应用中说明了这些工具。研究人员可以将δ*与点估计和标准误差一起报告，作为衡量协变量偏移下外部有效性的第三个数字。
英文摘要：
This paper studies the robustness of estimated policy effects to changes in the distribution of covariates, a key determinant of the external validity of (quasi)-experimental results. I propose a novel robustness metric $δ^*$ which measures the smallest covariate shift needed to invalidate an empirical claim about the policy effect (e.g., $ATE > 0$). I estimate $δ^*$ via de-biased GMM, achieving a parametric rate of convergence while accommodating machine-learning estimators of treatment-effect heterogeneity (e.g., LASSO, random forests, neural networks). I develop benchmarking and calibration exercises to interpret the magnitude of $δ^*$. I illustrate these tools in an application to the Oregon Health Insurance Experiment. Researchers can report $δ^*$ alongside the point estimate and standard error as a third number gauging external validity under covariate shifts.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
