Understanding causal mechanisms is essential for explaining and generalizing empirical phenomena. Causal mediation analysis offers statistical techniques to quantify mediation effects. However, existing methods typically require strong identification assumptions or sophisticated research designs. We develop a new identification strategy that simplifies these assumptions, enabling the simultaneous estimation of causal and mediation effects. The strategy is based on a novel decomposition of total treatment effects, which transforms the challenging mediation problem into a simple linear regression problem. The new method establishes a new link between causal mediation and causal moderation. We discuss several research designs and estimators to increase the usability of our identification strategy for a variety of empirical studies. We demonstrate the application of our method by estimating the causal mediation effect in experiments concerning common pool resource governance and voting information. Additionally, we have created statistical software to facilitate the implementation of our method.

翻译：理解因果机制对于解释和推广实证现象至关重要。因果中介分析提供了量化中介效应的统计技术。然而，现有方法通常需要较强的识别假设或复杂的研究设计。我们提出了一种新的识别策略，简化了这些假设，使得能够同时估计因果效应和中介效应。该策略基于总处理效应的一种新颖分解，将具有挑战性的中介问题转化为简单的线性回归问题。新方法建立了因果中介与因果调节之间的新联系。我们讨论了多种研究设计和估计量，以提高我们识别策略在各种实证研究中的可用性。我们通过估计关于公共池资源治理和投票信息的实验中的因果中介效应，展示了我们方法的应用。此外，我们开发了统计软件以促进我们方法的实施。