标题： 在乘法工具变量模型下非线性反事实函数的推断 显示英文标题

标题： Inference on Nonlinear Counterfactual Functionals under a Multiplicative IV Model

摘要： 工具变量（IV）方法在因果推断中起着核心作用，特别是在处理分配受未观察变量混淆的设置中。 IV 方法近年来得到了广泛的发展，并被应用于从经济学到流行病学的多个领域。 在这项工作中，我们研究了最近引入的乘法 IV（MIV）模型，并展示了其在平均处理效应以外的因果推断中的效用。 特别是，我们表明它能够对由矩方程表征的广泛反事实泛函进行识别和推断。 这包括对分位数处理效应的推断。 我们开发了对这些泛函进行高效且多重稳健估计的方法，并提供了具有渐近有效性的推断程序。 实验结果表明，所提出的程序即使在中等样本量下也能表现良好。 显示英文摘要

摘要： Instrumental variable (IV) methods play a central role in causal inference, particularly in settings where treatment assignment is confounded by unobserved variables. IV methods have been extensively developed in recent years and applied across diverse domains, from economics to epidemiology. In this work, we study the recently introduced multiplicative IV (MIV) model and demonstrate its utility for causal inference beyond the average treatment effect. In particular, we show that it enables identification and inference for a broad class of counterfactual functionals characterized by moment equations. This includes, for example, inference on quantile treatment effects. We develop methods for efficient and multiply robust estimation of such functionals, and provide inference procedures with asymptotic validity. Experimental results demonstrate that the proposed procedure performs well even with moderate sample sizes.