Fetching the paper…
Reading the bibliography…
We consider recovering causal structure from multivariate observational data.
Distinctness of the eigenvalues of a quadratic form in a multivariate sample
Masashi Okamoto · 1973
Earlier work this paper cites.
Structural equations with latent variables
Kenneth A. Bollen · 1989
Earlier work this paper cites.
Equivalence and synthesis of causal models
Thomas Verma and Judea Pearl · 1990
Earlier work this paper cites.
Causation, prediction, and search
Peter Spirtes, Clark Glymour, and Richard Scheines · 2000
Earlier work this paper cites.
A new identification condition for recursive models with correlated errors
Carlos Brito and Judea Pearl · 2002
Earlier work this paper cites.
Ancestral graph Markov models
Thomas Richardson and Peter Spirtes · 2002
Earlier work this paper cites.
Measuring statistical dependence with Hilbert-Schmidt norms
Arthur Gretton, Olivier Bousquet, Alex Smola, and Bernhard Schölkopf · 2005
Earlier work this paper cites.
Identifying direct causal effects in linear models
Jin Tian · 2005
Earlier work this paper cites.
A linear non-Gaussian acyclic model for causal discovery
Shohei Shimizu, Patrik O. Hoyer, Aapo Hyvärinen, and Antti Kerminen · 2006
Earlier work this paper cites.
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
Earlier work this paper cites.
On the completeness of orientation rules for causal discovery in the presence of latent confounders and selection bias
Jiji Zhang · 2008
Earlier work this paper cites.
Markov equivalence for ancestral graphs
R. Ayesha Ali, Thomas S. Richardson, and Peter Spirtes · 2009
Earlier work this paper cites.
Computing maximum likelihood estimates in recursive linear models with correlated errors
Mathias Drton, Michael Eichler, and Thomas S. Richardson · 2009
Earlier work this paper cites.
Brownian distance covariance
Gábor J. Székely and Maria L. Rizzo · 2009
Earlier work this paper cites.
Discovering unconfounded causal relationships using linear non-gaussian models
Doris Entner and Patrik O. Hoyer · 2010
Earlier work this paper cites.
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
Earlier work this paper cites.
Learning high-dimensional directed acyclic graphs with latent and selection variables
Diego Colombo, Marloes H. Maathuis, Markus Kalisch, and Thomas S. Richardson · 2012
Cited alongside, same era.
lcmix: Layered and chained mixture models , 2012
Daniel Dvorkin · 2012
Cited alongside, same era.
Half-trek criterion for generic identifiability of linear structural equation models
Rina Foygel, Jan Draisma, and Mathias Drton · 2012
Cited alongside, same era.
Causal inference using graphical models with the R package pcalg
Markus Kalisch, Martin Mächler, Diego Colombo, Marloes H. Maathuis, and Peter Bühlmann · 2012
Cited alongside, same era.
Learning sparse causal models is not NP-hard
Tom Claassen, Joris M. Mooij, and Tom Heskes · 2013
Cited alongside, same era.
Pairwise likelihood ratios for estimation of non-Gaussian structural equation models
Aapo Hyvärinen and Stephen M. Smith · 2013
greedyBAPs: Greedy BAP Learning Using Penalised Maximum Likelihood Score , 2017
Christopher Nowzohour · 2017
Later among the works it cites.
Distributional equivalence and structure learning for bow-free acyclic path diagrams
Christopher Nowzohour, Marloes H. Maathuis, Robin J. Evans, and Peter Bühlmann · 2017
Later among the works it cites.
Empirical likelihood for linear structural equation models with dependent errors
Y. Samuel Wang and Mathias Drton · 2017
Later among the works it cites.
Algebraic problems in structural equation modeling
Mathias Drton · 2018
Later among the works it cites.
Kernel-based tests for joint independence
Niklas Pfister, Peter Bühlmann, Bernhard Schölkopf, and Jonas Peters · 2018
Later among the works it cites.
Linear Structural Equation Models with Non-Gaussian Errors: Estimation and Discovery
Y. Samuel Wang · 2018
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Geometry of the faithfulness assumption in causal inference
Caroline Uhler, Garvesh Raskutti, Peter Bühlmann, and Bin Yu · 2013
Cited alongside, same era.
A consistent test of independence based on a sign covariance related to Kendall’s tau
Wicher Bergsma and Angelos Dassios · 2014
Cited alongside, same era.
High-dimensional learning of linear causal networks via inverse covariance estimation
Po-Ling Loh and Peter Bühlmann · 2014
Cited alongside, same era.
Bayesian estimation of causal direction in acyclic structural equation models with individual-specific confounder variables and non-Gaussian distributions
Shohei Shimizu and Kenneth Bollen · 2014
Cited alongside, same era.
Introduction to nested markov models
Ilya Shpitser, Robin J Evans, Thomas S Richardson, and James M Robins · 2014
Cited alongside, same era.
ParceLiNGAM: a causal ordering method robust against latent confounders
Tatsuya Tashiro, Shohei Shimizu, Aapo Hyvärinen, and Takashi Washio · 2014
Cited alongside, same era.
Later among the works it cites.
On causal discovery with an equal-variance assumption
Wenyu Chen, Mathias Drton, and Y. Samuel Wang · 2019
Later among the works it cites.
MultiRNG: Multivariate Pseudo-Random Number Generation , 2019
Hakan Demirtas, Rawan Allozi, and Ran Gao · 2019
Later among the works it cites.
Markov properties for mixed graphical models
Robin Evans · 2019
Later among the works it cites.
Handbook of graphical models
Marloes Maathuis, Mathias Drton, Steffen Lauritzen, and Martin Wainwright, editors · 2019
Later among the works it cites.
Ordering-based causal structure learning in the presence of latent variables
Daniel Bernstein, Basil Saeed, Chandler Squires, and Caroline Uhler · 2020
Closest in time.
mvtnorm: Multivariate Normal and t Distributions , 2020
Alan Genz, Frank Bretz, Tetsuhisa Miwa, Xuefei Mi, Friedrich Leisch, Fabian Scheipl, and Torsten Hothorn · 2020
Closest in time.
Causal discovery of linear non-Gaussian acyclic models in the presence of latent confounders
Takashi Nicholas Maeda and Shohei Shimizu · 2020
Closest in time.
Learning linear non-Gaussian causal models in the presence of latent variables
Saber Salehkaleybar, AmirEmad Ghassami, Negar Kiyavash, and Kun Zhang · 2020
Closest in time.
High-dimensional causal discovery under non-Gaussianity
Y. Samuel Wang and Mathias Drton · 2020
Closest in time.
Causal structural learning via local graphs, 2021
Wenyu Chen, Mathias Drton, and Ali Shojaie · 2021
Closest in time.