标题： 具有通用工具变量的因果效应识别与去偏学习 显示英文标题

标题： Identification and Debiased Learning of Causal Effects with General Instrumental Variables

摘要： 工具变量方法在处理分配受未观测变量混淆的情况下，是因果推断的基础。 在本文中，我们开发了一个通用的非参数框架，用于具有多类别或连续工具变量的识别和学习。 具体而言，我们提出了一种加法工具变量框架，通过权重函数来识别平均潜在结果和平均处理效应。 利用半参数理论，我们推导了有效影响函数，并通过去偏机器学习构建了一致且渐近正态的估计量。 进一步发展了纵向数据、动态处理方案和乘法工具变量的扩展方法。 我们通过模拟研究和分析就业培训合作计划项目的真实数据来展示所提出的方法。 显示英文摘要

摘要： Instrumental variable methods are fundamental to causal inference when treatment assignment is confounded by unobserved variables. In this article, we develop a general nonparametric framework for identification and learning with multi-categorical or continuous instrumental variables. Specifically, we propose an additive instrumental variable framework to identify mean potential outcomes and the average treatment effect with a weighting function. Leveraging semiparametric theory, we derive efficient influence functions and construct consistent, asymptotically normal estimators via debiased machine learning. Extensions to longitudinal data, dynamic treatment regimes, and multiplicative instrumental variables are further developed. We demonstrate the proposed method by employing simulation studies and analyzing real data from the Job Training Partnership Act program.