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Causal discovery aims to recover causal structures or models underlying the observed data.
Pearson’s contribution to the theory of two factors
Charles Spearman · 1928
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Statistical methods for research workers
Ronald Aylmer Fisher · 1950
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Random variables and probability distributions
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Characterization problems in mathematical statistics
Abram M Kagan, Calyampudi Radhakrishna Rao, and Yurij Vladimirovich Linnik · 1973
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Structural Equations with Latent Variable
Kenneth A. Bollen · 1989
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An algorithm for fast recovery of sparse causal graphs
Peter Spirtes and Clark Glymour · 1991
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Causal inference in the presence of latent variables and selection bias
Peter Spirtes, Christopher Meek, and Thomas Richardson · 1995
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Optimal structure identification with greedy search
David Maxwell Chickering · 2002
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A linear non-Gaussian acyclic model for causal discovery
Shohei Shimizu, Patrik O Hoyer, Aapo Hyvärinen, and Antti Kerminen · 2006
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Learning the structure of linear latent variable models
Ricardo Silva, Richard Scheine, Clark Glymour, and Peter Spirtes · 2006
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Extensions of ICA for causality discovery in the hong kong stock market
K. Zhang and L. Chan · 2006
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A kernel statistical test of independence
Arthur Gretton, Kenji Fukumizu, Choon H Teo, Le Song, Bernhard Schölkopf, and Alex J Smola · 2008
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Estimation of causal effects using linear non-gaussian causal models with hidden variables
Patrik O Hoyer, Shohei Shimizu, Antti J Kerminen, and Markus Palviainen · 2008
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Nonlinear causal discovery with additive noise models
Patrik O Hoyer, Dominik Janzing, Joris M Mooij, Jonas Peters, and Bernhard Schölkopf · 2009
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Estimation of linear non-gaussian acyclic models for latent factors
Shohei Shimizu, Patrik O Hoyer, and Aapo Hyvärinen · 2009
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On the identifiability of the post-nonlinear causal model
Kun Zhang and Aapo Hyvärinen · 2009
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Structural equation modeling with amos: Basic concepts, applications, and programming
Learning high-dimensional directed acyclic graphs with latent and selection variables
Diego Colombo, Marloes H Maathuis, Markus Kalisch, and Thomas S Richardson · 2012
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Learning linear bayesian networks with latent variables
Animashree Anandkumar, Daniel Hsu, Adel Javanmard, and Sham Kakade · 2013
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Calculation of entailed rank constraints in partially non-linear and cyclic models
Peter Spirtes · 2013
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ParceLiNGAM: a causal ordering method robust against latent confounders
Tatsuya Tashiro, Shohei Shimizu, Aapo Hyvärinen, and Takashi Washio · 2014
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Causal clustering for 1-factor measurement models
Erich Kummerfeld and Joseph Ramsey · 2016
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Causal discovery and inference: concepts and recent methodological advances
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Barbara M Byrne · 2010
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Discovering unconfounded causal relationships using linear non-gaussian models
Doris Entner and Patrik O Hoyer · 2010
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Automated search for causal relations: Theory and practice
Peter Spirtes, Clark Glymour, Richard Scheines, and Robert Tillman · 2010
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Trek separation for gaussian graphical models
Seth Sullivant, Kelli Talaska, Jan Draisma, et al · 2010
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Learning causality and causality-related learning
Kun Zhang, Bernhard Schölkopf, Peter Spirtes, and Clark Glymour · 2010
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DirectLiNGAM: A direct method for learning a linear non-Gaussian structural equation model
Shohei Shimizu, Takanori Inazumi, Yasuhiro Sogawa, Aapo Hyvärinen, Yoshinobu Kawahara, Takashi Washio, Patrik O Hoyer, and Kenneth Bollen · 2011
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Peter Spirtes and Kun Zhang · 2016
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Causal discovery from nonstationary/heterogeneous data: Skeleton estimation and orientation determination
K. Zhang, B. Huang, J. Zhang, C. Glymour, and B. Schölkopf · 2017
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Triad constraints for learning causal structure of latent variables
Ruichu Cai, Feng Xie, Clark Glymour, Zhifeng Hao, and Kun Zhang · 2019
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The seven tools of causal inference, with reflections on machine learning
Judea Pearl · 2019
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Causal discovery from heterogeneous/nonstationary data
B. Huang*, K. Zhang*, J. Zhang, R. Sanchez-Romero, C. Glymour, and B. Schölkopf · 2020
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