标题： 连续工具变量的边缘因果效应估计 显示英文标题

标题： Marginal Causal Effect Estimation with Continuous Instrumental Variables

摘要： 工具变量（IVs）通常是连续的，出现在经济学、流行病学和社会科学等不同领域。 针对连续IVs的现有方法通常假设强烈的参数模型或假设处理效果同质，而在中等至高维协变量设置下，完全非参数方法可能表现不佳。 我们提出了一种新的框架，通过条件加权平均导数效应来识别连续IVs的平均处理效应。 利用条件Riesz表示器，我们的框架统一了连续和分类IVs。 在这个框架中，平均处理效应通常是过度识别的，导致一个具有非平凡切空间的半参数观测数据模型。 表征这个切空间需要对一个二阶参数子模型进行细致的构造，据我们所知，这在该文献中并不是标准做法。 在估计方面，基于半参数模型中的影响函数，该函数在子模型中也是局部有效的，我们开发了一个局部有效、三重稳健、有界且易于实现的估计量。 我们将这些方法应用于来自公主玛格丽特癌症中心的一项观察性临床研究，以检验肿瘤学中的所谓肥胖悖论，评估非小细胞肺癌患者中过多体重对两年死亡率的因果效应。 显示英文摘要

摘要： Instrumental variables (IVs) are often continuous, arising in diverse fields such as economics, epidemiology, and the social sciences. Existing approaches for continuous IVs typically impose strong parametric models or assume homogeneous treatment effects, while fully nonparametric methods may perform poorly in moderate- to high-dimensional covariate settings. We propose a new framework for identifying the average treatment effect with continuous IVs via conditional weighted average derivative effects. Using a conditional Riesz representer, our framework unifies continuous and categorical IVs. In this framework, the average treatment effect is typically overidentified, leading to a semiparametric observed-data model with a nontrivial tangent space. Characterizing this tangent space involves a delicate construction of a second-order parametric submodel, which, to the best of our knowledge, has not been standard practice in this literature. For estimation, building on an influence function in the semiparametric model that is also locally efficient within a submodel, we develop a locally efficient, triply robust, bounded, and easy-to-implement estimator. We apply our methods to an observational clinical study from the Princess Margaret Cancer Centre to examine the so-called obesity paradox in oncology, assessing the causal effect of excess body weight on two-year mortality among patients with non-small cell lung cancer.