Mediation Analysis for Multiple Causal Mechanisms

Masataka Taguri ()
Additional contact information
Masataka Taguri: Yokohama City University School of Medicine, Department of Biostatistics

A chapter in Frontiers of Biostatistical Methods and Applications in Clinical Oncology, 2017, pp 343-357 from Springer

Abstract: Abstract In many health studies, researchers are interested in estimating the treatment effects on the outcome around and through an intermediate variable. Such causal mediation analyses aim to understand the mechanisms that explain the treatment effect. Although multiple mediators are often involved in real studies, most of the literature considers mediation analyses with one mediator at a time. In this article, we review some recent advances in mediation analyses when there are multiple causal pathways. We discuss the cases that (1) there is a mediator-outcome confounder that is affected by the treatment when we are interested in one mediator and (2) there are causally non-ordered multiple mediators.

Keywords: Causal inference; Mediation analysis; Multiple mediators; Natural direct effect; Natural indirect effect (search for similar items in EconPapers)
Date: 2017
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-10-0126-0_21

Ordering information: This item can be ordered from
http://www.springer.com/9789811001260

DOI: 10.1007/978-981-10-0126-0_21

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().