Identification and Estimation of Causal Mechanisms and Net Effects of a Treatment Under Unconfoundedness
Identification and Estimation of Causal Mechanisms and Net Effects of a Treatment Under Unconfoundedness
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DOI：
10.2139/ssrn.1423353
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发表时间：
2009-06
期刊：
影响因子：
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通讯作者：
Carlos A. Flores;Alfonso Flores-Lagunes
中科院分区：
文献类型：
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作者：
Carlos A. Flores;Alfonso Flores-Lagunes
在分析治疗对结果的因果影响时，一个重要的目标是了解治疗因果作用的机制。我们使用潜在结果框架定义了治疗的因果机制效应和该机制的因果效应网。这些效应提供了对总体效应的直观分解，这对政策目的很有用。我们提供识别条件的基础上unconfoundedness假设来估计它们，在一个异质性的影响环境中，和一个随机分配的治疗的情况下，当选择到治疗是基于可观的。两个实证应用说明的概念和方法。
An important goal when analyzing the causal effect of a treatment on an outcome is to understand the mechanisms through which the treatment causally works. We define a causal mechanism effect of a treatment and the causal effect net of that mechanism using the potential outcomes framework. These effects provide an intuitive decomposition of the total effect that is useful for policy purposes. We offer identification conditions based on an unconfoundedness assumption to estimate them, within a heterogeneous effect environment, and for the cases of a randomly assigned treatment and when selection into the treatment is based on observables. Two empirical applications illustrate the concepts and methods.