Direction of Causation Between Shared and Non-Shared Environmental Factors

Ozaki, Katsuya; Ando, Jun

doi:10.1007/s10519-009-9257-0

Cited by 10 publications

(8 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(Duffy & Martin, 1994; Gillespie et al ., 2003; Neale et al ., 1994). Furthermore, in order for DOC analyses to yield clear‐cut results, there should be a large enough correlation between variables (Osaki & Ando, 2009) in addition to that the aetiologies for the variables need to differ (Heath et al ., 1993). In our case, it might be that the correlations between the variables were not high enough to obtain significantly different model fit between the different models in the DOC analyses.…”

Section: Discussionmentioning

confidence: 99%

Testing causal models of the relationship between childhood gender atypical behaviour and parent–child relationship

Alanko

Santtila

Salo

et al. 2010

British J of Dev Psycho

View full text Add to dashboard Cite

An association between childhood gender atypical behaviour (GAB) and a negative parent-child relationship has been demonstrated in several studies, yet the causal relationship of this association is not fully understood. In the present study, different models of causation between childhood GAB and parent-child relationships were tested. Direction of causation modelling was applied to twin data from a population-based sample (n= 2,565) of Finnish 33- to 43-year-old twins. Participants completed retrospective self-report questionnaires. Five different models of causation were then fitted to the data: GAB → parent-child relationship, parent-child relationship → GAB, reciprocal causation, a bivariate genetic model, and a model assuming no correlation. It was found that a model in which GAB and quality of mother-child, and father-child relationship reciprocally affect each other best fitted the data. The findings are discussed in light of how we should understand, including causality, the association between GAB and parent-child relationship.

show abstract

Section: Discussionmentioning

confidence: 99%

Testing causal models of the relationship between childhood gender atypical behaviour and parent–child relationship

Alanko

Santtila

Salo

et al. 2010

British J of Dev Psycho

View full text Add to dashboard Cite

show abstract

“…Another advantage is that nnSEM can detect the direction of causation between two cross-sectional variables. Ozaki and Ando (2009) used this advantage to develop a method to detect the direction of causations between C factors and between E factors in bivariate behavior genetics models.…”

Section: Nnsemmentioning

confidence: 99%

“…Therefore, neither the spurious correlation model nor the reciprocal causation model can be identified using SEM (number of degrees of freedom: −1). However, as shown in the Appendix of Ozaki and Ando (2009), nnSEM using second- and third-order moments can identify both models.…”

Section: Nnsemmentioning

confidence: 99%

See 1 more Smart Citation

Using Non-Normal SEM to Resolve the ACDE Model in the Classical Twin Design

et al. 2010

Self Cite

View full text Add to dashboard Cite

One of the biggest problems in classical twin studies is that it cannot estimate additive genetic (A), non-additive genetic (D), shared environmental (C), and non-shared environmental (E) effects, simultaneously, because the model, referred to as the ACDE model, has negative degrees of freedom when using Structural Equation Modeling (SEM). Therefore, instead of the ACDE model, the ACE model or the ADE model is actually used. However, using the ACE or ADE models almost always leads to biased estimates. In the present paper, the univariate ACDE model is developed using non-normal Structural Equation Modeling (nnSEM). In SEM, (1st- and) 2nd-order moments, namely, (means and) covariances are used as information. However, nnSEM uses higher-order moments as well as (1st- and) 2nd-order moments. nnSEM has a number of advantages over SEM. One of which is that nnSEM can specify models that cannot be specified using SEM because of the negative degrees of freedom. Simulation studies have shown that the proposed method can decrease the biases. There are other factors that have possible effects on phenotypes, such as higher-order epistasis. Since the proposed method cannot estimate these effects, further research on developing a more exhaustive model is needed.

show abstract

“…These methods have, in turn, led to the development of many additional methods, including latent confounder methods (Hoyer et al, 2008b;Shimizu and Hyvärinen, 2008), time series methods (Hyvärinen et al, 2010), nonlinear methods Zhang and Hyvärinen, 2009b;Tillman et al, 2010) and discrete variable methods (Peters et al, 2011a). These non-Gaussian methods have been applied to the data studied in many fields, including economics (Ferkingsta et al, 2011;Moneta et al, 2013), behavior genetics (Ozaki and Ando, 2009;Ozaki et al, 2011), psychology (Takahashi et al, 2012), environmental science (Niyogi et al, 2010), epidemiology (Rosenström et al, 2012), neuroscience (Smith et al, 2011) and biology (Statnikov et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Lingam: Non-Gaussian Methods for Estimating Causal Structures

Shimizu

2014

Behaviormetrika

View full text Add to dashboard Cite

In many empirical sciences, the causal mechanisms underlying various phenomena need to be studied. Structural equation modeling is a general framework used for multivariate analysis, and provides a powerful method for studying causal mechanisms. However, in many cases, classical structural equation modeling is not capable of estimating the causal directions of variables. This is because it explicitly or implicitly assumes Gaussianity of data and typically utilizes only the covariance structure of data. In many applications, however, non-Gaussian data are often obtained, which means that more information may be contained in the data distribution than the covariance matrix is capable of containing. Thus, many new methods have recently been proposed for utilizing the non-Gaussian structure of data and estimating the causal directions of variables. In this paper, we provide an overview of such recent developments in causal inference, and focus in particular on the non-Gaussian methods known as LiNGAM.

show abstract

Direction of Causation Between Shared and Non-Shared Environmental Factors

Cited by 10 publications

References 22 publications

Testing causal models of the relationship between childhood gender atypical behaviour and parent–child relationship

Testing causal models of the relationship between childhood gender atypical behaviour and parent–child relationship

Using Non-Normal SEM to Resolve the ACDE Model in the Classical Twin Design

Lingam: Non-Gaussian Methods for Estimating Causal Structures

Contact Info

Product

Resources

About