Diabetes-Ameliorating Effects of Fermented Red Ginseng and Causal Effects on Hormonal Interactions: Testing the Hypothesis by Multiple Group Path Analysis

2013 ◽  
Vol 16 (5) ◽  
pp. 383-395 ◽  
Author(s):  
Kwang Jo Lee ◽  
Sook Yeon Lee ◽  
Geun Eog Ji
Keyword(s):  
Path Analysis ◽  
Causal Effects ◽  
Red Ginseng ◽  
Multiple Group ◽  
Multiple Group Path Analysis ◽  
Hormonal Interactions

scholarly journals Direct and indirect causal effects of heterozygosity on fitness-related traits in Alpine ibex

2015 ◽  
Vol 282 (1798) ◽  
pp. 20141873 ◽  
Author(s):  
Alice Brambilla ◽  
Iris Biebach ◽  
Bruno Bassano ◽  
Giuseppe Bogliani ◽  
Achaz von Hardenberg
Keyword(s):  
Path Analysis ◽  
Body Mass ◽  
Causal Effects ◽  
Parasite Resistance ◽  
Wild Populations ◽  
Capra Ibex ◽  
Alpine Ibex ◽  
Horn Growth ◽  
Nematode Eggs ◽  
Free Ranging

Heterozygosity–fitness correlations (HFCs) are a useful tool to investigate the effects of inbreeding in wild populations, but are not informative in distinguishing between direct and indirect effects of heterozygosity on fitness-related traits. We tested HFCs in male Alpine ibex ( Capra ibex ) in a free-ranging population (which suffered a severe bottleneck at the end of the eighteenth century) and used confirmatory path analysis to disentangle the causal relationships between heterozygosity and fitness-related traits. We tested HFCs in 149 male individuals born between 1985 and 2009. We found that standardized multi-locus heterozygosity (MLH), calculated from 37 microsatellite loci, was related to body mass and horn growth, which are known to be important fitness-related traits, and to faecal egg counts (FECs) of nematode eggs, a proxy of parasite resistance. Then, using confirmatory path analysis, we were able to show that the effect of MLH on horn growth was not direct but mediated by body mass and FEC. HFCs do not necessarily imply direct genetic effects on fitness-related traits, which instead can be mediated by other traits in complex and unexpected ways.

scholarly journals Disentangling indirect effects through multiple mediators without assuming any causal structure among the mediators

2019 ◽  
Author(s):  
Wen Wei Loh ◽  
Beatrijs Moerkerke ◽  
Tom Loeys ◽  
Stijn Vansteelandt
Keyword(s):  
Path Analysis ◽  
Indirect Effect ◽  
Indirect Effects ◽  
Causal Structure ◽  
Analytical Form ◽  
Causal Effects ◽  
Causal Interpretation ◽  
Mutual Dependence ◽  
Causal Pathway ◽  
Definition Of

When multiple mediators exist on the causal pathway from treatment to outcome, path analysis prevails for disentangling indirect effects along paths linking possibly several mediators. However, separately evaluating each indirect effect along different posited paths demands stringent assumptions, such as correctly specifying the mediators' causal structure, and no unobserved confounding among the mediators. These assumptions may be unfalsifiable in practice and, when they fail to hold, can result in misleading conclusions about the mediators. Nevertheless, these assumptions are avoidable when substantive interest is in inference about the indirect effects specific to each distinct mediator. In this article, we introduce a new definition of indirect effects called interventional indirect effects from the causal inference and epidemiology literature. Interventional indirect effects can be unbiasedly estimated without the assumptions above while retaining scientifically meaningful interpretations. We show that under a typical class of linear and additive mean models, estimators of interventional indirect effects adopt the same analytical form as prevalent product-of-coefficient estimators assuming a parallel mediator model. Prevalent estimators are therefore unbiased when estimating interventional indirect effects - even when there are unknown causal effects among the mediators - but require a different causal interpretation. When other mediators moderate the effect of each mediator on the outcome, and the mediators' covariance is affected by treatment, such an indirect effect due to the mediators' mutual dependence (on one another) cannot be attributed to any mediator alone. We exploit the proposed definitions of interventional indirect effects to develop novel estimators under such settings.

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