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Constraint-based causal discovery methods leverage conditional independence tests to infer causal relationships in a wide variety of applications.
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DAGs with NO TEARS: Continuous Optimization for Structure Learning
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J. Zhang and P. L. Spirtes · 2012
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Representation learning: A review and new perspectives
Y. Bengio, A. Courville, and P. Vincent · 2013
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J. Peters, J. M. Mooij, D. Janzing, and B. Schölkopf · 2014
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Causal inference using invariant prediction: identification and confidence intervals
J. Peters and N. Meinshausen · 2016
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Behind distribution shift: Mining driving forces of changes and causal arrows
B. Huang, K. Zhang, J. Zhang, R. Sanchez-Romero, C. Glymour, and B. Schölkopf · 2017
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Elements of Causal Inference: Foundations and Learning Algorithms
J. Peters, D. Janzing, and B. Schölkopf · 2017
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Causal discovery from nonstationary/heterogeneous data: Skeleton estimation and orientation determination
K. Zhang, B. Huang, J. Zhang, C. Glymour, and B. Schölkopf · 2017
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Language models are few-shot learners
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, et al · 2020
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Object conditioning for causal inference
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Causal discovery with general non-linear relationships using non-linear ica
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P. Spirtes and T. S. Richardson · 2021
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Weakly supervised causal representation learning
J. Brehmer, P. De Haan, P. Lippe, and T. S. Cohen · 2022
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