Traditionally in the social sciences, causal mediation
analysis has been formulated, understood, and implemented within the
framework of linear structural equation models. We argue and
demonstrate that this is problematic for three reasons; the lack of
a general definition of causal mediation effects independent of a
particular statistical model, the inability to specify the key
identification assumption, and the difficulty of extending the
framework to nonlinear models. In this paper, we propose an
alternative approach that overcomes these limitations. Our approach
is general because it offers the definition, identification,
estimation, and sensitivity analysis of causal mediation effects
without reference to any specific statistical model. Further, our
approach explicitly links these four elements closely together
within a single framework. As a result, the proposed framework can
accommodate linear and nonlinear relationships, parametric and
nonparametric models, continuous and discrete mediators, and various
types of outcome variables. The general definition and
identification result also allow us to develop sensitivity analysis
in the context of commonly used models, which enables applied
researchers to formally assess the robustness of their empirical
conclusions to violations of the key assumption. We illustrate our
approach by applying it to the Job Search Intervention Study (JOBS
II). We also offer easy-to-use software that implements all of our
proposed methods.
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