用元学习方法更准确估计差异中的差异模型中处理效应的异质性。
A Meta-learner for Heterogeneous Effects in Difference-in-Differences
- 基于双重稳健框架，将处理效应估计转化为凸优化问题。
- 在条件平行趋势下，对任意变量子集灵活建模，提升估计精度。
- 对辅助模型误差鲁棒，适合复杂面板数据中的因果推断任务。
针对面板数据中异质处理效应的估计问题，本文在条件平行趋势假设下采用经典的双重差分（DiD）框架。提出一种新型双重稳健的元学习器，用于估计处理组的条件平均处理效应（CATT），将问题转化为包含一组辅助模型的凸风险最小化。该框架支持在任意感兴趣的变量子集上灵活估计CATT，利用通用机器学习方法。通过尼曼正交性设计，所提方法对辅助模型的估计误差具有鲁棒性。作为主结果的推广，进一步发展了在协变量偏移下估计一般条件函数的元学习方法，并扩展至存在不遵从性的工具变量双重差分设置。实证结果表明，该方法优于现有基线。
原文摘要 · Abstract (English)
We address the problem of estimating heterogeneous treatment effects in panel data, adopting the popular Difference-in-Differences (DiD) framework under the conditional parallel trends assumption. We propose a novel doubly robust meta-learner for the Conditional Average Treatment Effect on the Treated (CATT), reducing the estimation to a convex risk minimization problem involving a set of auxiliary models. Our framework allows for the flexible estimation of the CATT, when conditioning on any subset of variables of interest using generic machine learning. Leveraging Neyman orthogonality, our proposed approach is robust to estimation errors in the auxiliary models. As a generalization to our main result, we develop a meta-learning approach for the estimation of general conditional functionals under covariate shift. We also provide an extension to the instrumented DiD setting with non-compliance. Empirical results demonstrate the superiority of our approach over existing baselines.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。