论文标题
使用工具时间序列识别因果效应：滋扰IV和过去的校正
Identifying Causal Effects using Instrumental Time Series: Nuisance IV and Correcting for the Past
论文作者
论文摘要
仪器变量（IV）回归依靠工具来从没有观察的混杂的观察数据中推断出因果效应。我们考虑时间序列模型中的IV回归，例如向量自动回归（VAR）过程。 I.I.D.的直接申请技术通常是不一致的，因为它们过去无法正确调整依赖项。在本文中，我们概述了由于时间结构而引起的困难，并提出了构建识别方程的方法，这些方程可用于时间序列数据中因果效应的一致参数估计。一种方法使用额外的滋扰协变量来获得可识别性（即使在I.I.D.案件中也可能感兴趣的想法）。我们进一步提出了一个图形边缘化框架，该框架使我们能够以原则上的时间序列方式应用滋扰IV和其他IV方法。我们的方法利用了Global Markov属性的版本，我们证明该版本适用于VAR（P）过程。对于VAR（1）过程，我们证明了与Jordan形式相关的可识别性条件，并且与I.I.D的众所周知的等级条件不同。案例（例如，它们不需要与协变量一样多的乐器）。我们提供方法，证明它们的一致性，并展示如何将推断的因果效应用于分布泛化。模拟实验证实了我们的理论结果。我们提供现成的Python代码。
Instrumental variable (IV) regression relies on instruments to infer causal effects from observational data with unobserved confounding. We consider IV regression in time series models, such as vector auto-regressive (VAR) processes. Direct applications of i.i.d. techniques are generally inconsistent as they do not correctly adjust for dependencies in the past. In this paper, we outline the difficulties that arise due to time structure and propose methodology for constructing identifying equations that can be used for consistent parametric estimation of causal effects in time series data. One method uses extra nuisance covariates to obtain identifiability (an idea that can be of interest even in the i.i.d. case). We further propose a graph marginalization framework that allows us to apply nuisance IV and other IV methods in a principled way to time series. Our methods make use of a version of the global Markov property, which we prove holds for VAR(p) processes. For VAR(1) processes, we prove identifiability conditions that relate to Jordan forms and are different from the well-known rank conditions in the i.i.d. case (they do not require as many instruments as covariates, for example). We provide methods, prove their consistency, and show how the inferred causal effect can be used for distribution generalization. Simulation experiments corroborate our theoretical results. We provide ready-to-use Python code.
