Social
scientists are often interested in testing multiple causal
mechanisms through which a treatment affects outcomes. A
predominant approach has been to use linear structural equation
models and examine the statistical significance of corresponding
path coefficients. However, this approach implicitly assumes that
the multiple mechanisms are causally independent of one another. In
this paper, we consider a set of alternative assumptions that are
sufficient to identify the average causal mediation effects when
multiple, causally related mediators exist. We develop a new
sensitivity analysis for examining the robustness of empirical
findings to the potential violation of a key identification
assumption. We apply the proposed methods to three political
psychology experiments which examine alternative causal pathways
between media framing and public opinion. Our analysis reveals that
the validity of original conclusions is highly reliant on the
assumed independence of alternative causal mechanisms, highlighting
the importance of proposed sensitivity analysis. All of the
proposed methods can be implemented via an open source R package,
mediation.