Extracting Mechanisms from Heterogeneous Effects: An Identification Strategy for Mediation Analysis

Understanding causal mechanisms is crucial for explaining and generalizing empirical phenomena. Causal mediation analysis offers statistical techniques to quantify the mediation effects. However, current methods often require multiple ignorability assumptions or sophisticated research designs. In this paper, we introduce a novel identification strategy that enables the simultaneous identification and estimation of treatment and mediation effects. By combining explicit and implicit mediation analysis, this strategy exploits heterogeneous treatment effects through a new decomposition of total treatment effects. Monte Carlo simulations demonstrate that the method is more accurate and precise across various scenarios. To illustrate the efficiency and efficacy of our method, we apply it to estimate the causal mediation effects in two studies with distinct data structures, focusing on common pool resource governance and voting information. Additionally, we have developed statistical software to facilitate the implementation of our method.