Estimation of causal structures in longitudinal data using non-Gaussianity

Kadowaki, Kento; Shimizu, Shohei; Washio, Takashi

doi:10.1109/mlsp.2013.6661912

Cited by 4 publications

(1 citation statement)

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“…Ramsey et al (2011) obtained excellent estimation results on simulated functional magnetic resonance imaging (fMRI) data created by Smith et al (2011). Furthermore, Kadowaki et al (2013) proposed an approach for estimating time-varying causal structures, based on longitudinal data, which is a type of three-way data where variables are repeatedly measured for the same subjects and at different time points. Kawahara et al (2010) proposed a LiNGAM analysis of groups of variables, instead of simply single variables.…”

Section: Three-way Data Modelsmentioning

confidence: 99%

Lingam: Non-Gaussian Methods for Estimating Causal Structures

Shimizu

2014

Behaviormetrika

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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.

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Section: Three-way Data Modelsmentioning

confidence: 99%