Identification of causal effects using instrumental variables
Identification of causal effects using instrumental variables
复制标题
DOI：
10.2307/2291629
复制
发表时间：
1996-06-01
影响因子：
3.7
通讯作者：
Rubin, DB
中科院分区：
文献类型：
--
作者：
Angrist, JD;Imbens, GW;Rubin, DB
我们概述了一个框架的因果推理的设置分配到一个二进制的治疗是可验证的，但遵守分配是不完美的，使接收的治疗是不可验证的。为了解决与通过可验证的分配（一种“意向治疗分析”）来比较受试者相关的问题，我们使用了工具变量，经济学家长期以来一直在使用具有恒定治疗效应的回归模型。我们发现，工具变量（IV）估计可以嵌入鲁宾因果模型（RCM），并在一些简单和易于解释的假设下，IV估计是一个子群的单位，编译器的平均因果效应。在没有这些假设的情况下，IV被估量只是意向治疗因果被估量的比率，没有解释为平均因果效应。在RCM中嵌入IV方法的优点是，它澄清了因果解释所需的关键假设的性质，而且允许我们以直接的方式考虑结果对关键假设偏差的敏感性。我们应用我们的分析来估计退伍军人地位在越南时代对死亡率的影响，使用彩票号码，分配优先级的草案作为一种工具，我们用我们的结果来调查的结论的敏感性关键假设。
We outline a framework for causal inference in settings where assignment to a binary treatment is ignorable, but compliance with the assignment is not perfect so that the receipt of treatment is nonignorable. To address the problems associated with comparing subjects by the ignorable assignment - an ''intention-to-treat analysis'' - we make use of instrumental variables, which have long been used by economists in the context of regression models with constant treatment effects. We show that the instrumental variables (IV) estimand can be embedded within the Rubin Causal Model (RCM) and that under some simple and easily interpretable assumptions, the IV estimand is the average causal effect for a subgroup of units, the compliers. Without these assumptions, the IV estimand is simply the ratio of intention-to-treat causal estimands with no interpretation as an average causal effect. The advantages of embedding the IV approach in the RCM are that it clarifies the nature of critical assumptions needed for a causal interpretation, and moreover allows us to consider sensitivity of the results to deviations from key assumptions in a straightforward manner. We apply our analysis to estimate the effect of veteran status in the Vietnam era on mortality, using the lottery number that assigned priority for the draft as an instrument, and we use our results to investigate the sensitivity of the conclusions to critical assumptions.