标题： 将结构因果推断与机器学习相结合 S-DIDML 估计量用于异质处理效应 显示英文标题

标题： Bridging Structural Causal Inference and Machine Learning The S-DIDML Estimator for Heterogeneous Treatment Effects

摘要： 针对政策环境复杂性的增加和高维数据的激增，本文介绍了S-DIDML估计量，这是一个基于结构且半参数灵活的因果推断框架。 通过将双重差分（DID）逻辑嵌入到双重机器学习（DML）架构中，S-DIDML方法结合了时间识别的优势、基于机器学习的干扰调整以及正交化估计。 我们首先识别现有方法中的关键局限性，包括机器学习模型缺乏结构可解释性、经典DID在高维混杂因素下的不稳定性，以及标准DML框架的时间刚性。 基于最近在交错采用设计和Neyman正交化方面的进展，S-DIDML提供了一个五步估计流程，在保持可解释性和可扩展性的同时，能够稳健地估计异质处理效应（HTEs）。 讨论了在劳动经济学、教育、税收和环境政策领域的示范应用。 所提出的框架通过提供在复杂数据环境中具有政策相关性、结构可解释性和统计有效性的因果分析蓝图，为方法论前沿做出了贡献。 显示英文摘要

摘要： In response to the increasing complexity of policy environments and the proliferation of high-dimensional data, this paper introduces the S-DIDML estimator a framework grounded in structure and semiparametrically flexible for causal inference. By embedding Difference-in-Differences (DID) logic within a Double Machine Learning (DML) architecture, the S-DIDML approach combines the strengths of temporal identification, machine learning-based nuisance adjustment, and orthogonalized estimation. We begin by identifying critical limitations in existing methods, including the lack of structural interpretability in ML models, instability of classical DID under high-dimensional confounding, and the temporal rigidity of standard DML frameworks. Building on recent advances in staggered adoption designs and Neyman orthogonalization, S-DIDML offers a five-step estimation pipeline that enables robust estimation of heterogeneous treatment effects (HTEs) while maintaining interpretability and scalability. Demonstrative applications are discussed across labor economics, education, taxation, and environmental policy. The proposed framework contributes to the methodological frontier by offering a blueprint for policy-relevant, structurally interpretable, and statistically valid causal analysis in complex data settings.