Using Instrumental Variables for Inference About Policy Relevant Treatment Effects
Using Instrumental Variables for Inference About Policy Relevant Treatment Effects
复制标题
使用工具变量来推断政策相关的治疗效果
DOI：
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复制
发表时间：
2017
期刊：
影响因子：
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通讯作者：
Alexander Torgovitsky
中科院分区：
文献类型：
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作者：
M. Mogstad;Andrés Santos;Alexander Torgovitsky
我们提出了一种使用工具变量（IV）的方法来推断除受手头工具影响的个体之外的个体的因果效应。策略相关性和外部有效性取决于可靠地执行此操作的能力。我们的方法利用了IV估计和许多处理参数都可以表示为相同潜在边际处理效果的加权平均值的洞察力。由于确定了权重，对IV估计的了解通常会对未知的边际处理效果施加一些限制，从而对感兴趣的处理参数的值施加一些限制。我们展示了如何从IV估计中提取有关感兴趣的处理参数的信息，更一般地说，从一类IV - like估计中提取信息，其中包括两阶段最小二乘和普通最小二乘估计等。我们的方法有几个应用。首先，它可以用来构造假设政策变化的平均因果效应的非参数边界。其次，我们的方法允许研究人员灵活地结合形状限制和参数假设，从而使编译器的平均效应外推到不同或更大的群体的平均效应。第三，我们的方法可用于测试模型规格和关于行为的假设，例如无选择偏差和/或无增益选择。
We propose a method for using instrumental variables (IV) to draw inference about causal effects for individuals other than those affected by the instrument at hand. Policy relevance and external validity turn on the ability to do this reliably. Our method exploits the insight that both the IV estimand and many treatment parameters can be expressed as weighted averages of the same underlying marginal treatment effects. Since the weights are identified, knowledge of the IV estimand generally places some restrictions on the unknown marginal treatment effects, and hence on the values of the treatment parameters of interest. We show how to extract information about the treatment parameter of interest from the IV estimand and, more generally, from a class of IV‐like estimands that includes the two stage least squares and ordinary least squares estimands, among others. Our method has several applications. First, it can be used to construct nonparametric bounds on the average causal effect of a hypothetical policy change. Second, our method allows the researcher to flexibly incorporate shape restrictions and parametric assumptions, thereby enabling extrapolation of the average effects for compliers to the average effects for different or larger populations. Third, our method can be used to test model specification and hypotheses about behavior, such as no selection bias and/or no selection on gain.
DOI：
10.1073/pnas.96.8.4730
发表时间：
1999-04-13
影响因子：
11.1
作者：
Heckman, JJ;Vytlacil, EJ
通讯作者：
Vytlacil, EJ
评估边际政策变化和边际个人治疗的平均效果
DOI：
10.3386/w15211
发表时间：
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期刊：
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影响因子：
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作者：
Carneiro P
通讯作者：
Carneiro P