Hernán Miguel A, Robins James M
Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts 02115, USA.
Epidemiology. 2006 Jul;17(4):360-72. doi: 10.1097/01.ede.0000222409.00878.37.
The use of instrumental variable (IV) methods is attractive because, even in the presence of unmeasured confounding, such methods may consistently estimate the average causal effect of an exposure on an outcome. However, for this consistent estimation to be achieved, several strong conditions must hold. We review the definition of an instrumental variable, describe the conditions required to obtain consistent estimates of causal effects, and explore their implications in the context of a recent application of the instrumental variables approach. We also present (1) a description of the connection between 4 causal models-counterfactuals, causal directed acyclic graphs, nonparametric structural equation models, and linear structural equation models-that have been used to describe instrumental variables methods; (2) a unified presentation of IV methods for the average causal effect in the study population through structural mean models; and (3) a discussion and new extensions of instrumental variables methods based on assumptions of monotonicity.
使用工具变量（IV）方法很有吸引力，因为即使存在未测量的混杂因素，此类方法仍可一致地估计暴露对结局的平均因果效应。然而，要实现这种一致估计，必须满足几个严格的条件。我们回顾工具变量的定义，描述获得因果效应一致估计所需的条件，并在工具变量方法的近期应用背景下探讨其含义。我们还呈现：（1）对用于描述工具变量方法的4种因果模型——反事实、因果有向无环图、非参数结构方程模型和线性结构方程模型——之间联系的描述；（2）通过结构均值模型对研究人群中平均因果效应的IV方法进行统一呈现；以及（3）基于单调性假设对工具变量方法的讨论和新扩展。
