A0205
Title: Testing identification in mediation and dynamic treatment models
Authors: Kevin Kloiber - LMU Munich (Germany) [presenting]
Martin Huber - University of Fribourg (Switzerland)
Lukas Laffers - Matej Bel University (Slovakia)
Abstract: A test is proposed for the identification of causal effects in mediation and dynamic treatment models based on two sets of observed variables, namely covariates to be controlled for and suspected instruments, building on a previous test 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. The framework extends to post-treatment sample selection or attrition problems when replacing the mediator with 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. We apply our method to Slovak labor market data and find that our testable implications are not rejected for a typical sequence of training programs
