  An essential goal of program evaluation and scientific research is the
investigation of causal mechanisms. Over the past several decades, causal
mediation analysis has been used in medical and social sciences to decompose
the treatment effect into the natural direct and indirect effects. However, all
of the existing mediation analysis methods rely on parametric modeling
assumptions in one way or another, typically requiring researchers to specify
multiple regression models involving the treatment, mediator, outcome, and
pre-treatment confounders. To overcome this limitation, we propose a novel
nonparametric estimation method for causal mediation analysis that eliminates
the need for applied researchers to model multiple conditional distributions.
The proposed method balances a certain set of empirical moments between the
treatment and control groups by weighting each observation; in particular, we
establish that the proposed estimator is globally semiparametric efficient. We
also show how to consistently estimate the asymptotic variance of the proposed
estimator without additional efforts. Finally, we extend the proposed method to
other relevant settings including the causal mediation analysis with multiple
mediators.
