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Abstract:
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In assessing causal mediation effects, a challenge is that there can be more than one mediator on pathways from treatment to outcome. More precisely, we don't know exactly how many mediators are in the causal path and how they relate to each other. A few approaches have been proposed to estimate direct and indirect effects in the presence of two causally independent or dependent mediators. However, those methods cannot be generalized to settings of more than two mediators where causally independent and dependent mediators coexist. We propose a novel approach to identify direct and indirect effects under a general situation of multiple mediators: two causally dependent mediators (V,W) and one causally independent mediator (M). With our proposed sequential ignorability assumption, the overall treatment effect can be decomposed into direct and mediator-specific indirect effects. A sensitivity analysis strategy is developed for testing the proposed identifying assumptions. We can try to apply this method to the pollination data. We may use this approach to estimate the effect of a particular emission control technology, that installed on power plants, on ambient pollution.
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