论文标题
添加剂模型的公正估计
Unbiased estimation for additive exposure models
论文作者
论文摘要
因果推断方法已应用于研究人员想要估计治疗效应的各个领域。在传统的因果推理环境中，人们假设单位的结果不取决于其他单位的治疗方法。但是，随着因果推理方法扩展到更多的应用程序，因此需要对一般因果效应的估计量更大。我们使用曝光映射框架[Aronow and Samii，2017]来绘制治疗分配与潜在结果之间的关系。在暴露模型下，我们建议在治疗效果是加性的假设下，提出了一般因果效应的线性无偏估计量（LUE）。添加性提供了统计优势，即现已接触的对比，因此一组估计器认为增长了。我们确定了构成欧元仿射基础的欧元子集的子集，并通过定义估计器支持条件来表征具有最小整合差异的最佳款项。我们通过模拟表明，我们提出的估计器对违反添加性假设的行为相当强大，并且总的来说，利用所有暴露的信息都有好处。
Causal inference methods have been applied in various fields where researchers want to estimate treatment effects. In traditional causal inference settings, one assumes that the outcome of a unit does not depend on treatments of other units. However, as causal inference methods are extended to more applications, there is a greater need for estimators of general causal effects. We use an exposure mapping framework [Aronow and Samii, 2017] to map the relationship between the treatment allocation and the potential outcomes. Under the exposure model, we propose linear unbiased estimators (LUEs) for general causal effects under the assumption that treatment effects are additive. Additivity provides statistical advantages, where contrasts in exposures are now equivalent, and so the set of estimators considered grows. We identify a subset of LUEs that forms an affine basis for LUEs, and we characterize optimal LUEs with minimum integrated variance through defining conditions on the support of the estimator. We show, through simulations that our proposed estimators are fairly robust to violations of the additivity assumption, and in general, there is benefit in leveraging information from all exposures.
