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Statistical Causal Discovery: LiNGAM Approach

Part of the SpringerBriefs in Statistics Book Series

DOI: 10.1007/978-4-431-55784-5

ISBN: 978-4-431-55783-8

Publisher: Springer Japan - Springer Journals

Sep 5, 2022

Available

Introduction

Shimizu, Shohei

Pages: 1 –12

[In recent years, causal inference frameworks, such as the potential outcome and structural causal models, have increasingly been used to describe assumptions and concepts in machine learning. This chapter introduces basic concepts of statistical causal inference. It first introduces a...
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Basic LiNGAM Model

Shimizu, Shohei

Pages: 15 –29

[This chapter describes a structural causal model called linear non-Gaussian acyclic model (LiNGAM). It introduces the basic LiNGAM model without hidden common causes, assuming all common causes of observed variables are measured. In this basic LiNGAM model, the causal graph is identifiable...
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Estimation of the Basic LiNGAM Model

Shimizu, Shohei

Pages: 31 –48

[This chapter discusses estimation methods for the coefficient matrix B\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek}...
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Evaluation of Statistical Reliability and Model Assumptions

Shimizu, Shohei

Pages: 49 –57

[This chapter discusses how to evaluate the statistical reliability of estimated causal graphs and detect possible violations of model assumptions to improve the proposed models. Both are useful and essential when applying causal discovery methods to real-world data.]
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LiNGAM with Hidden Common Causes

Shimizu, Shohei

Pages: 61 –82

[This chapter discusses methods for estimating the causal structure of observed variables given hidden or unobserved common causes. Specifically, it introduces the LiNGAM model with hidden common causes and its estimation methods. It also discusses latent or unobserved variables in LiNGAM other...
Available

Other Extensions

Shimizu, Shohei

Pages: 83 –94

[This chapter summarizes the related topics from the previous chapters. Specifically, it discusses extensions of the LiNGAM model to cyclic, time-series, nonlinear, and discrete variable cases.]
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Correction to: Statistical Causal Discovery: LiNGAM Approach

Shimizu, Shohei

Pages: C1 –C1

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