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Recent work has shown promising results in causal discovery by leveraging interventional data with gradient-based methods, even when the intervened variables are unknown.
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Differentiable causal discovery from interventional data
P. Brouillard, S. Lachapelle, A. Lacoste, S. L. Julien, and A. Drouin · 2007
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Learning high-dimensional directed acyclic graphs with latent and selection variables, 2012
D. Colombo, M. H. Maathuis, M. Kalisch, and T. S. Richardson · 2012
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Characterization and greedy learning of interventional markov equivalence classes of directed acyclic graphs
A. Hauser and P. Bühlmann · 2012
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Estimating or propagating gradients through stochastic neurons for conditional computation, 2013
Y. Bengio, N. Léonard, and A. Courville · 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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Categorical reparameterization with gumbel-softmax, 2017
E. Jang, S. Gu, and B. Poole · 2017
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J. Peters, D. Janzing, and B. Schölkopf · 2017
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A. van den Oord, O. Vinyals, and K. Kavukcuoglu · 2017
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T. S. Verma and J. Pearl · 2013
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C. W. Huang, D. Krueger, A. Lacoste, and A. C. Courville · 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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Structural agnostic modeling: Adversarial learning of causal graphs, 2020
D. Kalainathan, O. Goudet, I. Guyon, D. Lopez-Paz, and M. Sebag · 2020
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Learning neural causal models from unknown interventions, 2020
N. R. Ke, O. Bilaniuk, A. Goyal, S. Bauer, H. Larochelle, B. Schölkopf, M. C. Mozer, C. Pal, and Y. Bengio · 2020
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Joris M. Mooij, Sara Magliacane, and Tom Claassen · 2020
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Masked gradient-based causal structure learning, 2020
I. Ng, Z. Fang, S. Zhu, Z. Chen, and J. Wang · 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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Addendum on the scoring of gaussian directed acyclic graphical models, 2021
J. Kuipers, G. Moffa, and D. Heckerman · 2021
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