Identification of Causal Mechanisms through an RD Approach

Causal Mediation Analysis has important implications in economics. It helps to deeply understand the policy makers' decisions and to better de- sign policy strategies. However, the identification process is not an easy issue and analyzing causal mechanisms requires stronger assumptions than evaluating the classical average treatment effect. The main difficulty con- sists in the endogeneity of the mediator with the consequence that it is not possible to identify the effects of interest. Several methods have been de- veloped, based on different set of assumptions and with different strategies for the estimation. I propose a new identification strategy for the estima- tion of the direct and the indirect effect, through an implementation of a Regression Discontinuity Design. I present two different models. The first one follows the traditional identification strategy based on linear equation models. The second model follows the most recent literature based on non- parametric identification procedures. I show the consistency of this last estimator, validating the results through a Monte Carlo simulation study.