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Bayesian causal structure learning aims to learn a posterior distribution over directed acyclic graphs (DAGs), and the mechanisms that define the relationship between parent and child variables.
Abcd-strategy: Budgeted experimental design for targeted causal structure discovery, 2019
Raj Agrawal, Chandler Squires, Karren Yang, Karthik Shanmugam, and Caroline Uhler · 1902
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DAG-GNN: DAG structure learning with graph neural networks
Yue Yu, Jie Chen, Tian Gao, and Mo Yu · 1904
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Gradient-based neural DAG learning
Sébastien Lachapelle, Philippe Brouillard, Tristan Deleu, and Simon Lacoste-Julien · 1906
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On random graphs i
P Erdös and A Rényi · 1959
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Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Judea Pearl · 1988
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On the equivalence of causal models
Tom S. Verma and Judea Pearl · 1990
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Causation, Prediction, and Search , volume 81
Peter Spirtes, Clark Glymour, and Richard Scheines · 1993
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Bayesian graphical models for discrete data
David Madigan and Jeremy York · 1995
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Data analysis with bayesian networks: A bootstrap approach
Nir Friedman, Moisés Goldszmidt, and Abraham J. Wyner · 1999
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Being bayesian about network structure
Nir Friedman and Daphne Koller · 2000
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Causality: Models, Reasoning, and Inference
Judea Pearl · 2000
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Causation, Prediction, and Search
P. Spirtes, C. Glymour, and R. Scheines · 2000
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Optimal structure identification with greedy search
David Maxwell Chickering · 2003
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Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networks
Dirk Husmeier · 2003
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Export-led growth and the japanese economy: evidence from var and directed acyclic graphs
Titus O. Awokuse · 2005
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Causal protein-signaling networks derived from multiparameter single-cell data
Karen Sachs, Omar D. Perez, Dana Pe’er, Douglas A. Lauffenburger, and Garry P. Nolan · 2005
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A linear non-gaussian acyclic model for causal discovery
Shohei Shimizu, Patrik O. Hoyer, Aapo Hyvarinen, and Antti Kerminen · 2006
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The max-min hill-climbing bayesian network structure learning algorithm
Ioannis Tsamardinos, Laura Brown, and Constantin Aliferis · 2006
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Nonlinear causal discovery with additive noise models
Patrik Hoyer, Dominik Janzing, Joris M Mooij, Jonas Peters, and Bernhard Schölkopf · 2008
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Dan Geiger and David Heckerman · 2013
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Stochastic variational inference
Matthew D Hoffman, David M Blei, Chong Wang, and John Paisley · 2013
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Identifiability of gaussian structural equation models with equal error variances
Learning neural causal models with active interventions, 2021
Nino Scherrer, Olexa Bilaniuk, Yashas Annadani, Anirudh Goyal, Patrick Schwab, Bernhard Schölkopf, Michael C. Mozer, Yoshua Bengio, Stefan Bauer, and Nan Rosemary Ke · 2021
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Bayesian Networks with Examples in R
Marco Scutari and Jean-Baptiste Denis · 2021
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Consistency guarantees for greedy permutation-based causal inference algorithms, 2021
Liam Solus, Yuhao Wang, and Caroline Uhler · 2021
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Learning bayesian networks: Search methods and experimental results
David Maxwell Chickering, Dan Geiger, and David Heckerman · 2022
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Bayesian Structure Learning with Generative Flow Networks
Tristan Deleu, António Góis, Chris Emezue, Mansi Rankawat, Simon Lacoste-Julien, Stefan Bauer, and Yoshua Bengio · 2022
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J. Peters and P. Bühlmann · 2013
Cited alongside, same era.
beta-vae: Learning basic visual concepts with a constrained variational framework
Irina Higgins, Loïc Matthey, Arka Pal, Christopher P. Burgess, Xavier Glorot, Matthew M. Botvinick, Shakir Mohamed, and Alexander Lerchner · 2016
Cited alongside, same era.
Elements of Causal Inference: Foundations and Learning Algorithms
Jonas Peters, Dominik Janzing, and Bernhard Schölkopf · 2017
Cited alongside, same era.
Dags with no tears: Continuous optimization for structure learning, 2018
Xun Zheng, Bryon Aragam, Pradeep Ravikumar, and Eric P. Xing · 2018
Cited alongside, same era.
Learning Neural Causal Models from Unknown Interventions
Nan Rosemary Ke, Olexa Bilaniuk, Anirudh Goyal, Stefan Bauer, H. Larochelle, Chris Pal, and Yoshua Bengio · 2019
Cited alongside, same era.
Differentiable causal discovery from interventional data
Philippe Brouillard, Sébastien Lachapelle, Alexandre Lacoste, Simon Lacoste-Julien, and Alexandre Drouin · 2020
Cited alongside, same era.
Dagbagm: Learning directed acyclic graphs of mixed variables with an application to identify prognostic protein biomarkers in ovarian cancer
Shrabanti Chowdhury, Ru Wang, Qing Yu, Catherine J. Huntoon, Larry M. Karnitz, Scott H. Kaufmann, Steven P. Gygi, Michael J. Birrer, Amanda G. Paulovich, Jie Peng, and Pei Wang · 2020
Cited alongside, same era.
Nan Rosemary Ke, Silvia Chiappa, Jane X. Wang, Jörg Bornschein, Théophane Weber, Anirudh Goyal, Matthew Botvinic, Michael Curtis Mozer, and Danilo Jimenez Rezende · 2022
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Greedy relaxations of the sparsest permutation algorithm, 2022
Wai-Yin Lam, Bryan Andrews, and Joseph Ramsey · 2022
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Learning GFlowNets from partial episodes for improved convergence and stability
Kanika Madan, Jarrid Rector-Brooks, Maksym Korablyov, Emmanuel Bengio, Moksh Jain, Andrei Nica, Tom Bosc, Yoshua Bengio, and Nikolay Malkin · 2022
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Trajectory Balance: Improved Credit Assignment in GFlowNets
Nikolay Malkin, Moksh Jain, Emmanuel Bengio, Chen Sun, and Yoshua Bengio · 2022
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Learning latent structural causal models
Jithendaraa Subramanian, Yashas Annadani, Ivaxi Sheth, Nan Rosemary Ke, Tristan Deleu, Stefan Bauer, Derek Nowrouzezahrai, and Samira Ebrahimi Kahou · 2022
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Interventions, where and how? experimental design for causal models at scale, 2022
Panagiotis Tigas, Yashas Annadani, Andrew Jesson, Bernhard Schölkopf, Yarin Gal, and Stefan Bauer · 2022
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Active bayesian causal inference
Christian Toth, Lars Lorch, Christian Knoll, Andreas Krause, Franz Pernkopf, Robert Peharz, and Julius von Kügelgen · 2022
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Tractable uncertainty for structure learning
Benjie Wang, Matthew R Wicker, and Marta Kwiatkowska · 2022
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Generative Flow Networks for Discrete Probabilistic Modeling
Dinghuai Zhang, Nikolay Malkin, Zhen Liu, Alexandra Volokhova, Aaron Courville, and Yoshua Bengio · 2022
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Joint bayesian inference of graphical structure and parameters with a single generative flow network, 2023
Tristan Deleu, Mizu Nishikawa-Toomey, Jithendaraa Subramanian, Nikolay Malkin, Laurent Charlin, and Yoshua Bengio · 2023
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