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This paper studies the problem of learning causal structures from observational data.
An algorithm for fast recovery of sparse causal graphs
P. Spirtes and C. Glymour · 1991
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Causal inference and causal explanation with background knowledge
C. Meek · 1995
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Learning Bayesian networks is NP-complete
D. M. Chickering · 1996
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Nonlinear Programming
D. P. Bertsekas · 1999
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Causation, Prediction, and Search
P. Spirtes, C. N. Glymour, and R. Scheines · 2000
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Optimal structure identification with greedy search
D. M. Chickering · 2002
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Exact Bayesian structure discovery in Bayesian networks
M. Koivisto and K. Sood · 2004
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Simple statistical gradient-following algorithms for connectionist reinforcement learning
R. J. Williams · 2004
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Causal protein-signaling networks derived from multiparameter single-cell data
K. Sachs, O. Perez, D. Pe’er, D. A. Lauffenburger, and G. P. Nolan · 2005
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Finding optimal Bayesian networks by dynamic programming
A. Singh and A. Moore · 2005
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Ordering-based search: A simple and effective algorithm for learning Bayesian networks
M. Teyssier and D. Koller · 2005
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A linear non-Gaussian acyclic model for causal discovery
S. Shimizu, P. O. Hoyer, A. Hyvärinen, and A. Kerminen · 2006
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Search for additive nonlinear time series causal models
T. Chu and C. Glymour · 2008
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On the completeness of orientation rules for causal discovery in the presence of latent confounders and selection bias
J. Zhang · 2008
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Probabilistic Graphical Models: Principles and Techniques
D. Koller and N. Friedman · 2009
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Causality
J. Pearl · 2009
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On the identifiability of the post-nonlinear causal model
K. Zhang and A. Hyvärinen · 2009
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Understanding the difficulty of training deep feedforward neural networks
X. Glorot and Y. Bengio · 2010
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Kernel-based conditional independence test and application in causal discovery
K. Zhang, J. Peters, D. Janzing, and B. Schölkopf · 2012
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Estimating or propagating gradients through stochastic neurons for conditional computation
Y. Bengio, N. Léonard, and A. Courville · 2013
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Identifiability of Gaussian structural equation models with equal error variances
J. Peters and P. Bühlmann · 2013
Generalized score functions for causal discovery
B. Huang, K. Zhang, Y. Lin, B. Schölkopf, and C. Glymour · 2018
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Structural agnostic modeling: Adversarial learning of causal graphs
D. Kalainathan, O. Goudet, I. Guyon, D. Lopez-Paz, and M. Sebag · 2018
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Search for causal models
P. Spirtes and K. Zhang · 2018
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DAGs with NO TEARS: Continuous optimization for structure learning
X. Zheng, B. Aragam, P. Ravikumar, and E. P. Xing · 2018
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Review of causal discovery methods based on graphical models
C. Glymour, K. Zhang, and P. Spirtes · 2019
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Causal inference on time series using restricted structural equation models
J. Peters, D. Janzing, and B. Schölkopf · 2013
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CAM: Causal additive models, high-dimensional order search and penalized regression
P. Bühlmann, J. Peters, J. Ernest, et al · 2014
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Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
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Causal discovery with continuous additive noise models
J. Peters, J. M. Mooij, D. Janzing, and B. Schölkopf · 2014
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On estimation of functional causal models: General results and application to the post-nonlinear causal model
K. Zhang, Z. Wang, J. Zhang, and B. Schölkopf · 2015
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Categorical reparameterization with Gumbel-Softmax
E. Jang, S. Gu, and B. Poole · 2017
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N. R. Ke, O. Bilaniuk, A. Goyal, S. Bauer, H. Larochelle, C. Pal, and Y. Bengio · 2019
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A graph autoencoder approach to causal structure learning
I. Ng, S. Zhu, Z. Chen, and Z. Fang · 2019
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Detecting and quantifying causal associations in large nonlinear time series datasets
J. Runge, P. Nowack, M. Kretschmer, S. Flaxman, and D. Sejdinovic · 2019
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DAG-GNN: DAG structure learning with graph neural networks
Y. Yu, J. Chen, T. Gao, and M. Yu · 2019
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Gradient-based neural DAG learning
S. Lachapelle, P. Brouillard, T. Deleu, and S. Lacoste-Julien · 2020
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On the role of sparsity and DAG constraints for learning linear DAGs
I. Ng, A. Ghassami, and K. Zhang · 2020
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Learning sparse nonparametric DAGs
X. Zheng, C. Dan, B. Aragam, P. Ravikumar, and E. P. Xing · 2020
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Causal discovery with reinforcement learning
S. Zhu, I. Ng, and Z. Chen · 2020
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Ordering-based causal discovery with reinforcement learning
X. Wang, Y. Du, S. Zhu, L. Ke, Z. Chen, J. Hao, and J. Wang · 2021
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gCastle: A Python toolbox for causal discovery
K. Zhang, S. Zhu, M. Kalander, I. Ng, J. Ye, Z. Chen, and L. Pan · 2021
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