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We address the problem of causal discovery from data, making use of the recently proposed causal modeling framework of modular structural causal models (mSCM) to handle cycles, latent confounders and non-linearities.
Fusion, propagation and structuring in belief networks
J. Pearl · 1986
Earlier work this paper cites.
Causal Networks: Semantics and Expressiveness
T.S. Verma and J. Pearl · 1990
Earlier work this paper cites.
A Bayesian method for the induction of probabilistic networks from data
G. F. Cooper and E. Herskovits · 1992
Earlier work this paper cites.
Learning Gaussian networks
D. Geiger and D. Heckerman · 1994
Earlier work this paper cites.
Strong completeness and faithfulness in Bayesian networks
C. Meek · 1995
Earlier work this paper cites.
Directed cyclic graphical representations of feedback models
P. Spirtes · 1995
Earlier work this paper cites.
A discovery algorithm for directed cyclic graphs
T. Richardson · 1996
Earlier work this paper cites.
A Bayesian approach to causal discovery
D. Heckerman, C. Meek, and G. Cooper · 1999
Earlier work this paper cites.
Automated discovery of linear feedback models
T. Richardson and P. Spirtes · 1999
Earlier work this paper cites.
Causation, Prediction, and Search
P. Spirtes, C. Glymour, and R. Scheines · 2000
Earlier work this paper cites.
Markov properties for acyclic directed mixed graphs
T. Richardson · 2003
Earlier work this paper cites.
A probabilistic approach to the geometry of the l p n l^{n}_{p} -ball
F. Barthe, O. Guédon, S. Mendelson, and A. Naor · 2005
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Measure Theory. Vol. I, II
V.I. Bogachev · 2007
Cited alongside, same era.
Answer sets
M. Gelfond · 2008
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What is Answer Set Programming?
V. Lifschitz · 2008
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J. Zhang · 2008
Cited alongside, same era.
Computing maximum likelihood estimates in recursive linear models with correlated errors
M. Drton, M. Eichler, and T. Richardson · 2009
Cited alongside, same era.
Markovian acyclic directed mixed graphs for discrete data
R.J. Evans and T.S. Richardson · 2014
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Constraint-based causal discovery: Conflict resolution with answer set programming
A. Hyttinen, F. Eberhardt, and M. Järvisalo · 2014
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BACKSHIFT: Learning causal cyclic graphs from unknown shift interventions
D. Rothenhäusler, C. Heinze, J. Peters, and N. Meinshausen · 2015
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Graphs for margins of Bayesian networks
R.J. Evans · 2016
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Deep Learning
I. Goodfellow, Y. Bengio, and A. Courville · 2016
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Ancestral causal inference
S. Magliacane, T. Claassen, and J.M. Mooij · 2016
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Causality: Models, reasoning, and inference
J. Pearl · 2009
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Maximum likelihood fitting of acyclic directed mixed graphs to binary data
R. Evans and T. Richardson · 2010
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Causal discovery for linear cyclic models
A. Hyttinen, F. Eberhardt, and P.O. Hoyer · 2010
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Bayesian probabilities for constraint-based causal discovery
T. Claassen and T. Heskes · 2013
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Learning Sparse Causal Models is not NP-hard
T. Claassen, J.M. Mooij, and T. Heskes · 2013
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R.J. Evans · 2017
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Markov properties for graphical models with cycles and latent variables
P. Forré and J. M. Mooij · 2017
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Elements of Causal Inference: Foundation and Learning Algorithms
J. Peters, D. Janzing, and B. Schölkopf · 2017
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Theoretical aspects of cyclic structural causal models
S. Bongers, J. Peters, B. Schölkopf, and J. M. Mooij · 2018
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Joint causal inference from multiple contexts
J. M. Mooij, S. Magliacane, and T. Claassen · 2018
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