  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.
