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We study causal representation learning, the task of inferring latent causal variables and their causal relations from high-dimensional mixtures of the variables.
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K. Zhang, J. Peters, D. Janzing, and B. Schölkopf · 2011
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A. Gretton, K. M. Borgwardt, M. J. Rasch, B. Schölkopf, and A. Smola · 2012
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On causal and anticausal learning
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Quantifying causal influences
D. Janzing, D. Balduzzi, M. Grosse-Wentrup, and B. Schölkopf · 2013
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Domain generalization via invariant feature representation
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Visual causal feature learning
K. Chalupka, P. Perona, and F. Eberhardt · 2015
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Causal inference and the data-fusion problem
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Unsupervised feature extraction by time-contrastive learning and nonlinear ICA
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Distinguishing cause from effect using observational data: methods and benchmarks
J. M. Mooij, J. Peters, D. Janzing, J. Zscheischler, and B. Schölkopf · 2016
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Causal inference by using invariant prediction: identification and confidence intervals
J. Peters, P. Bühlmann, and N. Meinshausen · 2016
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Nonlinear ICA of temporally dependent stationary sources
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E. Jang, S. Gu, and B. Poole · 2017
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Experimental design for learning causal graphs with latent variables
M. Kocaoglu, K. Shanmugam, and E. Bareinboim · 2017
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Discovering causal signals in images
D. Lopez-Paz, R. Nishihara, S. Chintala, B. Schölkopf, and L. Bottou · 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 consistency of structural equation models
P. Rubenstein, S. Weichwald, S. Bongers, J. Mooij, D. Janzing, M. Grosse-Wentrup, and B. Schölkopf · 2017
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Self-supervised learning with data augmentations provably isolates content from style
J. von Kügelgen, Y. Sharma, L. Gresele, W. Brendel, B. Schölkopf, M. Besserve, and F. Locatello · 2021
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On calibration and out-of-domain generalization
Y. Wald, A. Feder, D. Greenfeld, and U. Shalit · 2021
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Desiderata for representation learning: A causal perspective
Y. Wang and M. I. Jordan · 2021
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CausalVAE: Disentangled representation learning via neural structural causal models
M. Yang, F. Liu, Z. Chen, X. Shen, J. Hao, and J. Wang · 2021
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Learning temporally causal latent processes from general temporal data
W. Yao, Y. Sun, A. Ho, C. Sun, and K. Zhang · 2021
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Recognition in terra incognita
S. Beery, G. Van Horn, and P. Perona · 2018
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Group invariance principles for causal generative models
M. Besserve, N. Shajarisales, B. Schölkopf, and D. Janzing · 2018
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Invariant causal prediction for nonlinear models
C. Heinze-Deml, J. Peters, and N. Meinshausen · 2018
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The book of why: the new science of cause and effect
J. Pearl and D. Mackenzie · 2018
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Invariant models for causal transfer learning
M. Rojas-Carulla, B. Schölkopf, R. Turner, and J. Peters · 2018
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DAGs with no tears: Continuous optimization for structure learning
X. Zheng, B. Aragam, P. Ravikumar, and E. Xing · 2018
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R. S. Zimmermann, Y. Sharma, S. Schneider, M. Bethge, and W. Brendel · 2021
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Weakly supervised representation learning with sparse perturbations
K. Ahuja, J. S. Hartford, and Y. Bengio · 2022
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On Pearl’s hierarchy and the foundations of causal inference
E. Bareinboim, J. D. Correa, D. Ibeling, and T. Icard · 2022
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Weakly supervised causal representation learning
J. Brehmer, P. De Haan, P. Lippe, and T. Cohen · 2022
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Function classes for identifiable nonlinear independent component analysis
S. Buchholz, M. Besserve, and B. Schölkopf · 2022
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Towards causal algorithmic recourse
A.-H. Karimi, J. von Kügelgen, B. Schölkopf, and I. Valera · 2022
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Identifiability of deep generative models without auxiliary information
B. Kivva, G. Rajendran, P. Ravikumar, and B. Aragam · 2022
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Partial disentanglement via mechanism sparsity
S. Lachapelle and S. Lacoste-Julien · 2022
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Disentanglement via mechanism sparsity regularization: A new principle for nonlinear ICA
S. Lachapelle, P. Rodriguez, Y. Sharma, K. E. Everett, R. Le Priol, A. Lacoste, and S. Lacoste-Julien · 2022
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Citris: Causal identifiability from temporal intervened sequences
P. Lippe, S. Magliacane, S. Löwe, Y. M. Asano, T. Cohen, and S. Gavves · 2022
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Identifying weight-variant latent causal models
Y. Liu, Z. Zhang, D. Gong, M. Gong, B. Huang, A. v. d. Hengel, K. Zhang, and J. Q. Shi · 2022
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Invariant causal representation learning for out-of-distribution generalization
C. Lu, Y. Wu, J. M. Hernández-Lobato, and B. Schölkopf · 2022
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Causal transportability for visual recognition
C. Mao, K. Xia, J. Wang, H. Wang, J. Yang, E. Bareinboim, and C. Vondrick · 2022
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Identifiable deep generative models via sparse decoding
G. E. Moran, D. Sridhar, Y. Wang, and D. Blei · 2022
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Causal discovery in heterogeneous environments under the sparse mechanism shift hypothesis
R. Perry, J. von Kügelgen, and B. Schölkopf · 2022
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Causality for machine learning
B. Schölkopf · 2022
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From statistical to causal learning
B. Schölkopf and J. von Kügelgen · 2022
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Weakly supervised disentangled generative causal representation learning
X. Shen, F. Liu, H. Dong, Q. Lian, Z. Chen, and T. Zhang · 2022
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Causal structure learning: a combinatorial perspective
C. Squires and C. Uhler · 2022
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Identifying patient-specific root causes with the heteroscedastic noise model
E. V. Strobl and T. A. Lasko · 2022
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Identification of linear non-gaussian latent hierarchical structure
F. Xie, B. Huang, Z. Chen, Y. He, Z. Geng, and K. Zhang · 2022
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Temporally disentangled representation learning
W. Yao, G. Chen, and K. Zhang · 2022
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Abstraction between structural causal models: A review of definitions and properties
F. M. Zennaro · 2022
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Interventional causal representation learning
K. Ahuja, D. Mahajan, Y. Wang, and Y. Bengio · 2023
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Effect identification in cluster causal diagrams
T. V. Anand, A. H. Ribeiro, J. Tian, and E. Bareinboim · 2023
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Provably learning object-centric representations
J. Brady, R. S. Zimmermann, Y. Sharma, B. Schölkopf, J. von Kügelgen, and W. Brendel · 2023
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Learning linear causal representations from interventions under general nonlinear mixing
S. Buchholz, G. Rajendran, E. Rosenfeld, B. Aragam, B. Schölkopf, and P. Ravikumar · 2023
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Spuriosity didn’t kill the classifier: Using invariant predictions to harness spurious features
C. Eastwood, S. Singh, A. L. Nicolicioiu, M. Vlastelica, J. von Kügelgen, and B. Schölkopf · 2023
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A. Hyvarinen, I. Khemakhem, and H. Morioka · 2023
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On the identifiability and estimation of causal location-scale noise models
A. Immer, C. Schultheiss, J. E. Vogt, B. Schölkopf, P. Bühlmann, and A. Marx · 2023
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Synergies between disentanglement and sparsity: Generalization and identifiability in multi-task learning
S. Lachapelle, T. Deleu, D. Mahajan, I. Mitliagkas, Y. Bengio, S. Lacoste-Julien, and Q. Bertrand · 2023
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Causal representation learning for instantaneous and temporal effects in interactive systems
P. Lippe, S. Magliacane, S. Löwe, Y. M. Asano, T. Cohen, and E. Gavves · 2023
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Disentanglement of correlated factors via hausdorff factorized support
K. Roth, M. Ibrahim, Z. Akata, P. Vincent, and D. Bouchacourt · 2023
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Linear causal disentanglement via interventions
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Score-based causal representation learning with interventions
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Causal component analysis
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Indeterminacy in generative models: Characterization and strong identifiability
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Jointly learning consistent causal abstractions over multiple interventional distributions
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