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.

翻译：本文提出了一种基于两组观测变量（即待控制的协变量与疑似工具变量）的中介与动态处理模型因果效应识别性检验方法，该方法建立在Huber与Kueck（2022）针对单一处理模型检验的基础之上。我们考虑包含处理变量与中介变量顺序分配机制的模型，以评估直接处理效应（排除中介变量影响）、间接处理效应（通过中介变量传导）或处理变量与中介变量的联合效应。我们建立了在观测数据中识别此类效应的可检验条件，这些条件共同蕴含：（1）在给定协变量条件下处理变量与中介变量的外生性；（2）处理变量与中介变量各自工具变量的有效性，即工具变量除通过处理变量或中介变量外不直接影响结果变量，且在给定协变量条件下不存在混淆。当将中介变量替换为观测结果的选择性指标时，本框架可扩展至处理后样本选择或损耗问题，从而实现对处理选择性与损耗选择性的联合检验。我们提出一种基于机器学习的检验方法，以数据驱动方式控制协变量，并通过模拟研究分析其有限样本表现。此外，我们将该方法应用于斯洛伐克劳动力市场数据，发现对于动态处理评估中通常考虑的系列培训项目，我们的可检验条件均未被拒绝。