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Consider a collection of datasets generated by unknown interventions on an unknown structural causal model $G$.
A bayesian method for constructing bayesian belief networks from databases
Gregory F Cooper and Edward Herskovits · 1991
Earlier work this paper cites.
Equivalence and synthesis of causal models
Thomas Verma and Judea Pearl · 1991
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Learning bayesian networks: The combination of knowledge and statistical data
David Heckerman, Dan Geiger, and David M Chickering · 1995
Earlier work this paper cites.
Causation, prediction, and search
Peter Spirtes, Clark N Glymour, Richard Scheines, and David Heckerman · 2000
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Causal discovery from changes
Jin Tian and Judea Pearl · 2001
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Optimal structure identification with greedy search
David Maxwell Chickering · 2002
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Parameter priors for directed acyclic graphical models and the characterization of several probability distributions
Dan Geiger, David Heckerman, et al · 2002
Earlier work this paper cites.
Introductory Lectures on Convex Optimization: A Basic Course
Yurii Nesterov · 2004
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Exact bayesian structure learning from uncertain interventions
Daniel Eaton and Kevin Murphy · 2007
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Graphical models, exponential families, and variational inference
Martin J Wainwright and Michael I Jordan · 2008
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Efficient online and batch learning using forward backward splitting
John Duchi and Yoram Singer · 2009
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Causality
Judea Pearl · 2009
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Composite objective mirror descent
John C Duchi, Shai Shalev-Shwartz, Yoram Singer, and Ambuj Tewari · 2010
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Causal inference using the algorithmic markov condition
Dominik Janzing and Bernhard Scholkopf · 2010
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On causal and anticausal learning
Bernhard Schölkopf, Dominik Janzing, Jonas Peters, Eleni Sgouritsa, Kun Zhang, and Joris Mooij · 2012
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Domain adaptation under target and conditional shift
Kun Zhang, Bernhard Schölkopf, Krikamol Muandet, and Zhikun Wang · 2013
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Proximal algorithms
Neal Parikh, Stephen Boyd, et al · 2014
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External validity: From do-calculus to transportability across populations
Judea Pearl and Elias Bareinboim · 2014
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Elements of causal inference: foundations and learning algorithms
Jonas Peters, Dominik Janzing, and Bernhard Schölkopf · 2017
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Domain adaptation by using causal inference to predict invariant conditional distributions
Sara Magliacane, Thijs van Ommen, Tom Claassen, Stephan Bongers, Philip Versteeg, and Joris M Mooij · 2018
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Amir Rosenfeld, Richard Zemel, and John K Tsotsos · 2018
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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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Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz · 2019
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Convex optimization: Algorithms and complexity
Sébastien Bubeck et al · 2015
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Estimating causal direction and confounding of two discrete variables
Krzysztof Chalupka, Frederick Eberhardt, and Pietro Perona · 2016
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Distinguishing cause from effect using observational data: Methods and benchmarks
Joris M. Mooij, Jonas Peters, Dominik Janzing, Jakob Zscheischler, and Bernhard Schölkopf · 2016
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Causal inference by using invariant prediction: identification and confidence intervals
Jonas Peters, Peter Bühlmann, and Nicolai Meinshausen · 2016
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Unified convergence analysis of stochastic momentum methods for convex and non-convex optimization
Tianbao Yang, Qihang Lin, and Zhe Li · 2016
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On perturbed proximal gradient algorithms
Yves F Atchadé, Gersende Fort, and Eric Moulines · 2017
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Learning neural causal models from unknown interventions
Nan Rosemary Ke, Olexa Bilaniuk, Anirudh Goyal, Stefan Bauer, Hugo Larochelle, Chris Pal, and Yoshua Bengio · 2019
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Causal dantzig: fast inference in linear structural equation models with hidden variables under additive interventions
Dominik Rothenhäusler, Peter Bühlmann, and Nicolai Meinshausen · 2019
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Causality for machine learning
Bernhard Schölkopf · 2019
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Permutation-based causal structure learning with unknown intervention targets
Chandler Squires, Yuhao Wang, and Caroline Uhler · 2019
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A meta-transfer objective for learning to disentangle causal mechanisms
Yoshua Bengio, Tristan Deleu, Nasim Rahaman, Rosemary Ke, Sébastien Lachapelle, Olexa Bilaniuk, Anirudh Goyal, and Christopher Pal · 2020
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Gradient-based neural DAG learning
Sébastien Lachapelle, Philippe Brouillard, Tristan Deleu, and Simon Lacoste-Julien · 2020
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