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Title: Causal mediation and sensitivity analysis for mixed-scale data
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 the 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.  more » « less
Award ID(s):
2144933
PAR ID:
10489739
Author(s) / Creator(s):
; ;
Publisher / Repository:
Sage Journals
Date Published:
Journal Name:
Statistical Methods in Medical Research
Volume:
32
Issue:
7
ISSN:
0962-2802
Page Range / eLocation ID:
1249 to 1266
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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