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Bayesian structure learning allows inferring Bayesian network structure from data while reasoning about the epistemic uncertainty -- a key element towards enabling active causal discovery and designing interventions in real world systems.
Masked gradient-based causal structure learning
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Simon Tong and Daphne Koller · 2001
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Dan Geiger and David Heckerman · 2002
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David Maxwell Chickering · 2003
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Being Bayesian About Network Structure. A Bayesian Approach to Structure Discovery in Bayesian Networks
Nir Friedman and Daphne Koller · 2003
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Improving markov chain monte carlo model search for data mining
Paolo Giudici and Robert Castelo · 2003
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Exact bayesian structure discovery in bayesian networks
Mikko Koivisto and Kismat Sood · 2004
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Model averaging for prediction with discrete bayesian networks
Denver Dash and Gregory F Cooper · 2004
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Karen Sachs, Omar Perez, Dana Pe’er, Douglas A Lauffenburger, and Garry P Nolan · 2005
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Chikako Van Koten and AR Gray · 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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Advances in exact bayesian structure discovery in bayesian networks
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Jack Kuipers and Giusi Moffa · 2017
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The concrete distribution: A continuous relaxation of discrete random variables
Chris J. Maddison, Andriy Mnih, and Yee Whye Teh · 2017
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Categorical reparametrization with gumbel-softmax
Eric Jang, Shixiang Gu, and Ben Poole · 2017
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DAGs with NO TEARS: Continuous optimization for structure learning
Xun Zheng, Bryon Aragam, Pradeep K Ravikumar, and Eric P Xing · 2018
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Structural agnostic modeling: Adversarial learning of causal graphs
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Bayesian structure learning using dynamic programming and MCMC
Daniel Eaton and Kevin Murphy · 2007
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Improving the structure MCMC sampler for Bayesian networks by introducing a new edge reversal move
Marco Grzegorczyk and Dirk Husmeier · 2008
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Learning causal bayesian network structures from experimental data
Byron Ellis and Wing Hung Wong · 2008
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Beware of the DAG!
A. Philip Dawid · 2010
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The random walk metropolis: linking theory and practice through a case study
Chris Sherlock, Paul Fearnhead, Gareth O Roberts, et al · 2010
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On the convergence of continuous constrained optimization for structure learning
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Diviyan Kalainathan, Olivier Goudet, Isabelle Guyon, David Lopez-Paz, and Michèle Sebag · 2018
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JAX: composable transformations of Python+NumPy programs, 2018
James Bradbury, Roy Frostig, Peter Hawkins, Matthew James Johnson, Chris Leary, Dougal Maclaurin, George Necula, Adam Paszke, Jake VanderPlas, Skye Wanderman-Milne, and Qiao Zhang · 2018
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ABCD-Strategy: Budgeted experimental design for targeted causal structure discovery
Raj Agrawal, Chandler Squires, Karren Yang, Karthikeyan Shanmugam, and Caroline Uhler · 2019
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Julius von Kügelgen, Paul K Rubenstein, Bernhard Schölkopf, and Adrian Weller · 2019
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DAG-GNN: DAG structure learning with graph neural networks
Yue Yu, Jie Chen, Tian Gao, and Mo Yu · 2019
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A graph autoencoder approach to causal structure learning
Ignavier Ng, Shengyu Zhu, Zhitang Chen, and Zhuangyan Fang · 2019
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D-vae: A variational autoencoder for directed acyclic graphs
Muhan Zhang, Shali Jiang, Zhicheng Cui, Roman Garnett, and Yixin Chen · 2019
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Learning neural causal models from unknown interventions
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Causal discovery toolbox: Uncover causal relationships in python
Diviyan Kalainathan and Olivier Goudet · 2019
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Gradient-based neural dag learning
Sébastien Lachapelle, Philippe Brouillard, Tristan Deleu, and Simon Lacoste-Julien · 2020
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Learning sparse nonparametric dags
Xun Zheng, Chen Dan, Bryon Aragam, Pradeep Ravikumar, and Eric Xing · 2020
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Dynotears: Structure learning from time-series data
Roxana Pamfil, Nisara Sriwattanaworachai, Shaan Desai, Philip Pilgerstorfer, Konstantinos Georgatzis, Paul Beaumont, and Bryon Aragam · 2020
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Causalvae: Disentangled representation learning via neural structural causal models
Mengyue Yang, Furui Liu, Zhitang Chen, Xinwei Shen, Jianye Hao, and Jun Wang · 2020
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Differentiable causal discovery from interventional data
Philippe Brouillard, Sébastien Lachapelle, Alexandre Lacoste, Simon Lacoste-Julien, and Alexandre Drouin · 2020
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Dags with no fears: A closer look at continuous optimization for learning bayesian networks
Dennis Wei, Tian Gao, and Yue Yu · 2020
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Monte carlo gradient estimation in machine learning
Shakir Mohamed, Mihaela Rosca, Michael Figurnov, and Andriy Mnih · 2020
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A meta-transfer objective for learning to disentangle causal mechanisms
Yoshua Bengio, Tristan Deleu, Nasim Rahaman, Nan Rosemary Ke, Sebastien Lachapelle, Olexa Bilaniuk, Anirudh Goyal, and Christopher Pal · 2020
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