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.

翻译：理解因果机制对于解释和推广经验现象至关重要。因果中介分析提供了量化中介效应的统计技术。然而，现有方法通常需要多重可忽略性假设或复杂的研究设计。本文提出了一种新颖的识别策略，能够同时识别和估计处理效应与中介效应。该策略通过结合显式和隐式中介分析，利用一种新的总处理效应分解方法来探究异质性处理效应。蒙特卡洛模拟表明，该方法在各种情境下均具有更高的准确性和精确度。为展示本方法的效率和有效性，我们将其应用于两项具有不同数据结构的研究中，以估计因果中介效应，研究重点集中于公共池塘资源治理和投票信息。此外，我们还开发了统计软件以促进本方法的实施。