论文标题
与治疗测量误差有关的因果推断：一种非参数仪器变量方法
Causal Inference with Treatment Measurement Error: A Nonparametric Instrumental Variable Approach
论文作者
论文摘要
当原因因错误破坏时，我们提出了基于内核的非参数估计量。我们通过在仪器变量设置中概括估计来做到这一点。尽管在测量误差和测量误差方面进行了重大研究，但在连续环境中处理未观察的混杂却是非平凡的：我们几乎没有看到先前的工作。作为我们调查的副产品，我们阐明了平均嵌入和特征功能之间的联系，以及一个同时学习如何允许一个人学习另一个之间的联系。这为内核方法研究开辟了道路，以利用特征功能估计的现有结果。最后，我们从经验上表明，我们提出的方法MEKIV在测量误差的强度和误差分布的类型上变化而对基准有所改善，并且在变化下是可靠的。
We propose a kernel-based nonparametric estimator for the causal effect when the cause is corrupted by error. We do so by generalizing estimation in the instrumental variable setting. Despite significant work on regression with measurement error, additionally handling unobserved confounding in the continuous setting is non-trivial: we have seen little prior work. As a by-product of our investigation, we clarify a connection between mean embeddings and characteristic functions, and how learning one simultaneously allows one to learn the other. This opens the way for kernel method research to leverage existing results in characteristic function estimation. Finally, we empirically show that our proposed method, MEKIV, improves over baselines and is robust under changes in the strength of measurement error and to the type of error distributions.
