Testing identification in mediation and dynamic treatment models
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中介模型与动态处理模型中的识别检验
- 研究问题
- 在处理和中介依次分配的观察数据中，如何利用协变量与疑似工具变量，检验直接效应、间接效应或联合效应的识别条件？
- 方法与思路
- 作者结合处理与中介各自的疑似工具变量建立可检验条件，用机器学习调整协变量，并将中介替换为观测选择指标分析流失。
- 材料陈述的贡献
- 摘要称，这些条件联合蕴含条件外生性与工具有效性；方法可扩展至处理后选择，并提供模拟和斯洛伐克培训项目应用。
- 阅读关注点
- 应核对可检验条件与外生性、排除限制之间的逻辑关系，工具变量的相关性要求，以及序贯处理和样本流失扩展的条件。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收均未核实；摘要未说明完整假设，应用中的未拒绝结果不能直接确证因果识别。
原文摘要
We propose a test for the identification of causal effects in mediation and dynamic treatment models that is based on two sets of observed variables, namely covariates to be controlled for and suspected instruments, building on the test by Huber and Kueck (2022) for single treatment models. We consider models with a sequential assignment of a treatment and a mediator to assess the direct treatment effect (net of the mediator), the indirect treatment effect (via the mediator), or the joint effect of both treatment and mediator. We establish testable conditions for identifying such effects in observational data. These conditions jointly imply (1) the exogeneity of the treatment and the mediator conditional on covariates and (2) the validity of distinct instruments for the treatment and the mediator, meaning that the instruments do not directly affect the outcome (other than through the treatment or mediator) and are unconfounded given the covariates. Our framework extends to post-treatment sample selection or attrition problems when replacing the mediator by a selection indicator for observing the outcome, enabling joint testing of the selectivity of treatment and attrition. We propose a machine learning-based test to control for covariates in a data-driven manner and analyze its finite sample performance in a simulation study. Additionally, we apply our method to Slovak labor market data and find that our testable implications are not rejected for a sequence of training programs typically considered in dynamic treatment evaluations.