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Causal discovery aims at revealing causal relations from observational data, which is a fundamental task in science and engineering.
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Clive WJ Granger · 1969
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Gideon Schwarz · 1978
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Testing for causality: A personal viewpoint
Clive WJ Granger · 1980
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Causal modeling with the tetrad program
Clark Glymour and Richard Scheines · 1986
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Theory refinement on bayesian networks
Wray Buntine · 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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The tetrad project: Constraint based aids to causal model specification
Richard Scheines, Peter Spirtes, Clark Glymour, Christopher Meek, and Thomas Richardson · 1998
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Causation, prediction, and search
Peter Spirtes, Clark N Glymour, Richard Scheines, and David Heckerman · 2000
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Optimal structure identification with greedy search
David Maxwell Chickering · 2002
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Searching for bayesian network structures in the space of restricted acyclic partially directed graphs
Silvia Acid and Luis M de Campos · 2003
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A linear non-Gaussian acyclic model for causal discovery
Shohei Shimizu, Patrik O Hoyer, Aapo Hyvärinen, Antti Kerminen, and Michael Jordan · 2006
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A simple approach for finding the globally optimal bayesian network structure
Tomi Silander and Petri Myllymäki · 2006
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The max-min hill-climbing bayesian network structure learning algorithm
Ioannis Tsamardinos, Laura E Brown, and Constantin F Aliferis · 2006
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Nonlinear causal discovery with additive noise models
Patrik Hoyer, Dominik Janzing, Joris M Mooij, Jonas Peters, and Bernhard Schölkopf · 2008
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On the identifiability of the post-nonlinear causal model
K Zhang and A Hyvärinen · 2009
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Estimation of a structural vector autoregression model using non-gaussianity
Aapo Hyvärinen, Kun Zhang, Shohei Shimizu, and Patrik O Hoyer · 2010
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Learning bayesian networks with the bnlearn R package
Marco Scutari · 2010
Tetrad—a toolbox for causal discovery
Joseph D Ramsey, Kun Zhang, Madelyn Glymour, Ruben Sanchez Romero, Biwei Huang, Imme Ebert-Uphoff, Savini Samarasinghe, Elizabeth A Barnes, and Clark Glymour · 2018
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Review of causal discovery methods based on graphical models
Clark Glymour, Kun Zhang, and Peter Spirtes · 2019
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Causal discovery in the presence of missing data
Ruibo Tu, Cheng Zhang, Paul Ackermann, Karthika Mohan, Hedvig Kjellström, and Kun Zhang · 2019
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bd2kccd/py-causal v1.2.1, December 2019
Chirayu (Kong) Wongchokprasitti, Harry Hochheiser, Jeremy Espino, Eamonn Maguire, Bryan Andrews, Michael Davis, and Chris Inskip · 2019
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Causal discovery from heterogeneous/nonstationary data
Biwei Huang, Kun Zhang, Jiji Zhang, Joseph D Ramsey, Ruben Sanchez-Romero, Clark Glymour, and Bernhard Schölkopf · 2020
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Causal discovery toolbox: Uncovering causal relationships in python
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Directlingam: A direct method for learning a linear non-gaussian structural equation model
Shohei Shimizu, Takanori Inazumi, Yasuhiro Sogawa, Aapo Hyvarinen, Yoshinobu Kawahara, Takashi Washio, Patrik O Hoyer, Kenneth Bollen, and Patrik Hoyer · 2011
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Kernel-based conditional independence test and application in causal discovery
Kun Zhang, Jonas Peters, Dominik Janzing, and Bernhard Schölkopf · 2011
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Causal inference using graphical models with the R package pcalg
Markus Kalisch, Martin Mächler, Diego Colombo, Marloes H. Maathuis, and Peter Bühlmann · 2012
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Learning optimal bayesian networks: A shortest path perspective
Changhe Yuan and Brandon Malone · 2013
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Generalized score functions for causal discovery
Biwei Huang, Kun Zhang, Yizhu Lin, Bernhard Schölkopf, and Clark Glymour · 2018
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Diviyan Kalainathan, Olivier Goudet, and Ritik Dutta · 2020
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Rcd: Repetitive causal discovery of linear non-gaussian acyclic models with latent confounders
Takashi Nicholas Maeda and Shohei Shimizu · 2020
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Generalized independent noise condition for estimating latent variable causal graphs
Feng Xie, Ruichu Cai, Biwei Huang, Clark Glymour, Zhifeng Hao, and Kun Zhang · 2020
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Causal additive models with unobserved variables
Takashi Nicholas Maeda and Shohei Shimizu · 2021
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Greedy relaxations of the sparsest permutation algorithm
Wai-Yin Lam, Bryan Andrews, and Joseph Ramsey · 2022
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py-tetrad, 2023
Bryan Andrew and Joseph Ramsey · 2023
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