An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controls
An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controls
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DOI：
10.1080/01621459.2021.1920957
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发表时间：
2021-05-30
影响因子：
3.7
通讯作者：
Zhu, Yinchu
中科院分区：
文献类型：
--
作者：
Chernozhukov, Victor;Wuthrich, Kaspar;Zhu, Yinchu
我们介绍了新的推理程序的反事实和综合控制方法的政策评估。我们将因果推理问题重新定义为一个反事实预测和一个结构突变检验问题。这使我们能够利用共形预测和结构突变测试的见解，开发置换推理程序，以适应现代高维估计，在弱和易于验证的条件下是有效的，并且可以证明对误指定是鲁棒的。我们的方法与许多不同的方法一起工作，在没有政策干预的情况下预测反事实的平均结果。例子包括综合控制，差异中的差异，因素和矩阵完成模型，（融合）时间序列面板数据模型。我们的方法在模拟中表现出出色的小样本性能，并被用于数据应用，我们重新评估室内卖淫合法化的后果。用于实现我们的共形推理方法的开源软件是可用的。
We introduce new inference procedures for counterfactual and synthetic control methods for policy evaluation. We recast the causal inference problem as a counterfactual prediction and a structural breaks testing problem. This allows us to exploit insights from conformal prediction and structural breaks testing to develop permutation inference procedures that accommodate modern high-dimensional estimators, are valid under weak and easy-to-verify conditions, and are provably robust against misspecification. Our methods work in conjunction with many different approaches for predicting counterfactual mean outcomes in the absence of the policy intervention. Examples include synthetic controls, difference-in-differences, factor and matrix completion models, and (fused) time series panel data models. Our approach demonstrates an excellent small-sample performance in simulations and is taken to a data application where we re-evaluate the consequences of decriminalizing indoor prostitution. Open-source software for implementing our conformal inference methods is available.