In a highly influential paper, Baron and Kenny (1986) proposed a
statistical procedure to conduct a causal mediation analysis and
identify possible causal mechanisms. This procedure has been widely
used across many branches of the social and medical sciences and
especially in psychology and epidemiology. However, one major
limitation of this approach is that it is based on a set of linear
regressions and cannot be easily extended to more complex situations
that are frequently encountered in applied research. In this paper,
we propose an approach that generalizes the Baron-Kenny
procedure. Our method can accommodate linear and nonlinear
relationships, parametric and nonparametric models, continuous and
discrete mediators, and various types of outcome variables. We also
provide a formal statistical justification for the proposed
generalization of the Baron-Kenny procedure by placing causal
mediation analysis within the widely-accepted counterfactual
framework of causal inference. Finally, we develop a set of
sensitivity analyses that allow applied researchers to quantify the
robustness of their empirical conclusions. Such sensitivity analysis
is important because as we show the Baron-Kenny procedure and our
generalization of it rest on a strong and untestable assumption even
in randomized experiments. We illustrate the proposed methods by
applying them to a randomized field experiment, the Job Search
Intervention Study (JOBS II). We also offer easy-to-use software
that implements all of our proposed methods.
(Last Revised July, 2009)
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