Abstract
We propose a novel approach for causal mediation analysis based on changes-in-changes assumptions restricting unobserved heterogeneity over time. This allows disentangling the causal effect of a binary treatment on a continuous outcome into an indirect effect operating through a binary intermediate variable (called mediator) and a direct effect running via other causal mechanisms. We identify average and quantile direct and indirect effects for various subgroups under the condition that the outcome is monotonic in the unobserved heterogeneity and that the distribution of the latter does not change over time conditional on the treatment and the mediator. We also provide a simulation study and two empirical applications regarding a training program evaluation and maternity leave reform.
Supplementary Materials
In the online appendices, we provide the proofs of Theorems 1–3. Furthermore, we demonstrate the finite sample performance of the proposed estimators in a simulation study and provide some background information about the applications. Additionally, we provide the replication package in a permanent online data repository (https://doi.org/10.7910/DVN/8ZPGHB).
Acknowledgments
We have benefited from comments by Giuseppe Germinario as well as conference/seminar participants at the Universities of Neuchâtel, Melbourne, Sydney, Hamburg, and Lisbon, the Luxembourg Institute of Socio-Economic Research, the 2019 meeting of the Austro-Swiss Region of the International Biometric Society in Lausanne, and the 2019 meeting of the International Association for Applied Econometrics in Nicosia.