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Basic LiNGAM Model
This chapter describes a structural causal model called linear non-Gaussian acyclic model (LiNGAM). It introduces the basic LiNGAM model without...
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LiNGAM with Hidden Common Causes
This chapter discusses methods for estimating the causal structure of observed variables given hidden or unobserved common causes. Specifically, it...
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Estimation of the Basic LiNGAM Model
This chapter discusses estimation methods for the coefficient matrix...
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Statistical Causal Discovery: LiNGAM Approach
This is the first book to provide a comprehensive introduction to a new semiparametric causal discovery approach known as LiNGAM, with the...
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Functional linear non-Gaussian acyclic model for causal discovery
In causal discovery, non-Gaussianity has been used to characterize the complete configuration of a linear non-Gaussian acyclic model (LiNGAM),...
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An estimation of causal structure based on Latent LiNGAM for mixed data
The linear non-gaussian acyclic model (LiNGAM) has been proposed as a method for estimating causal structures using structural equation modeling...
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Other Extensions
This chapter summarizes the related topics from the previous chapters. Specifically, it discusses extensions of the LiNGAM model to cyclic,...
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Causal order identification to address confounding: binary variables
This paper considers an extension of the linear non-Gaussian acyclic model (LiNGAM) that determines the causal order among variables from a dataset...
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Success concepts for causal discovery
Existing causal discovery algorithms are often evaluated using two success criteria, one that is typically unachievable and the other which is too...
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Introduction
In recent years, causal inference frameworks, such as the potential outcome and structural causal models, have increasingly been used to describe...
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Evaluation of Statistical Reliability and Model Assumptions
This chapter discusses how to evaluate the statistical reliability of estimated causal graphs and detect possible violations of model assumptions to...
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Causal modelling of heavy-tailed variables and confounders with application to river flow
Confounding variables are a recurrent challenge for causal discovery and inference. In many situations, complex causal mechanisms only manifest...
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Identifiability of latent-variable and structural-equation models: from linear to nonlinear
An old problem in multivariate statistics is that linear Gaussian models are often unidentifiable. In factor analysis, an orthogonal rotation of the...
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I-RCD: an improved algorithm of repetitive causal discovery from data with latent confounders
Discovering causal relationships from data affected by latent confounders is an important and difficult task. Until recently, approaches based on...
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Third moment-based causal inference
In observational data, covariance-based measures of dependence are of limited use for detecting reverse-causation (using y → x instead of x → y when...
