The goal of causal mediation analysis, often described within the potential outcomes framework, is to decompose the effect of an exposure on an outcome of interest along different causal pathways. Using the assumption of sequential ignorability to attain non-parametric identification, Imai et al. (2010) proposed a flexible approach to measuring mediation effects, focusing on parametric and semiparametric normal/Bernoulli models for the outcome and mediator. Less attention has been paid to the case where the outcome and/or mediator model are mixed-scale, ordinal, or otherwise fall outside the normal/Bernoulli setting. We develop a simple, but flexible, parametric modeling framework to accommodate the common situation where the responses are mixed continuous and binary, and apply it to a zero-one inflated beta model for the outcome and mediator. Applying our proposed methods to a publicly-available JOBS II dataset, we (i) argue for the need for non-normal models, (ii) show how to estimate both average and quantile mediation effects for boundary-censored data, and (iii) show how to conduct a meaningful sensitivity analysis by introducing unidentified, scientifically meaningful, sensitivity parameters.
翻译:伊马伊等人(2010年)提出一个灵活的方法来衡量调解效果,重点是结果和调解人的参数和半参数正常/伯尔努利模型;较少注意结果和(或)调解人模型具有混杂规模、交点或处于正常/伯诺利环境之外的情况;我们开发了一个简单但灵活的参数模型框架,以适应应对措施连续和二进制混合的常见情况,并将其应用于结果和调解人零一膨胀的贝塔模型;将我们提出的方法应用于公开使用的JOSBS II数据集;我们(一) 主张需要非正常模型;(二) 说明如何估计边界普查数据的平均和微调影响;以及(三) 说明如何通过引入不明、具有科学意义和敏感性的参数进行有意义的敏感性分析。