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Graphical causal inference as pioneered by Judea Pearl arose from research on artificial intelligence (AI), and for a long time had little connection to the field of machine learning.
A meta-transfer objective for learning to disentangle causal mechanisms
Y. Bengio, T. Deleu, N. Rahaman, R. Ke, S. Lachapelle, O. Bilaniuk, A. Goyal, and C. Pal, 2019 · 1901
Earlier work this paper cites.
Causal reasoning from meta-reinforcement learning
I. Dasgupta, J. Wang, S. Chiappa, J. Mitrovic, P. Ortega, D. Raposo, E. Hughes, P. Battaglia, M. Botvinick, and Z. Kurth-Nelson, 2019 · 1901
Earlier work this paper cites.
Learning robust representations by projecting superficial statistics out
H. Wang, Z. He, Z. C. Lipton, and E. P. Xing, 2019 · 1903
Earlier work this paper cites.
Optimal decision making under strategic behavior
M. Khajehnejad, B. Tabibian, B. Schölkopf, A. Singla, and M. Gomez-Rodriguez, 2019 · 1905
Earlier work this paper cites.
Semi-supervised learning, causality and the conditional cluster assumption
J. von Kügelgen, A. Mey, M. Loog, and B. Schölkopf, 2019 · 1905
Earlier work this paper cites.
Water vapor on the habitable-zone exoplanet K2-18b
B. Benneke, I. Wong, C. Piaulet, H. A. Knutson, I. J. M. Crossfield, J. Lothringer, C. V. Morley, P. Gao, T. P. Greene, C. Dressing, D. Dragomir, A. W. Howard, P. R. McCullough, E. M. R. K. J. J. Fortney, and J. Fraine, 2019 · 1909
Earlier work this paper cites.
Learning first-order symbolic representations for planning from the structure of the state space
B. Bonet and H. Geffner, 2019 · 1909
Earlier work this paper cites.
Recurrent independent mechanisms
A. Goyal, A. Lamb, J. Hoffmann, S. Sodhani, S. Levine, Y. Bengio, and B. Schölkopf, 2019 · 1909
Earlier work this paper cites.
Robust learning via cause-effect models
B. Schölkopf, D. Janzing, J. Peters, and K. Zhang, 2011 · 2011
Earlier work this paper cites.
Representation learning: A review and new perspectives
Y. Bengio, A. Courville, and P. Vincent, 2012 · 2012
Earlier work this paper cites.
Auto-encoding variational Bayes
D. P. Kingma and M. Welling, 2013 · 2013
Earlier work this paper cites.
Intriguing properties of neural networks
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus, 2013 · 2013
Cited alongside, same era.
External validity: From do-calculus to transportability across populations
J. Pearl and E. Bareinboim, 2015 · 2014
Cited alongside, same era.
Multi-level cause-effect systems
K. Chalupka, P. Perona, and F. Eberhardt, 2015 · 2015
Cited alongside, same era.
Independently controllable features
E. Bengio, V. Thomas, J. Pineau, D. Precup, and Y. Bengio, 2017 · 2017
Cited alongside, same era.
Toward a reputation state: The social credit system project of China
X. Dai, 2018 · 2018
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Evaluating reinforcement learning algorithms in observational health settings
O. Gottesman, F. Johansson, J. Meier, J. Dent, D. Lee, S. Srinivasan, L. Zhang, Y. Ding, D. Wihl, X. Peng, J. Yao, I. Lage, C. Mosch, L. wei H. Lehman, M. Komorowski, M. Komorowski, A. Faisal, L. A. Celi, D. Sontag, and F. Doshi-Velez, 2018 · 2018
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A survey of learning causality with data: Problems and methods
R. Guo, L. Cheng, J. Li, P. R. Hahn, and H. Liu, 2018 · 2018
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Generalization in anti-causal learning
N. Kilbertus, G. Parascandolo, and B. Schölkopf, 2018 · 2018
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Detecting and correcting for label shift with black box predictors
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P. Blöbaum, T. Washio, and S. Shimizu, 2016 · 2017
Cited alongside, same era.
Causal generative neural networks
O. Goudet, D. Kalainathan, P. Caillou, I. Guyon, D. Lopez-Paz, and M. Sebag, 2017 · 2017
Cited alongside, same era.
Conditional variance penalties and domain shift robustness
C. Heinze-Deml and N. Meinshausen, 2017 · 2017
Cited alongside, same era.
Invariant causal prediction for nonlinear models
C. Heinze-Deml, J. Peters, and N. Meinshausen, 2017 · 2017
Cited alongside, same era.
Causal learning
B. Schölkopf, 2017 · 2017
Cited alongside, same era.
Woulda, coulda, shoulda: Counterfactually-guided policy search
L. Buesing, T. Weber, Y. Zwols, S. Racaniere, A. Guez, J.-B. Lespiau, and N. Heess, 2018 · 2018
Cited alongside, same era.
Fast conditional independence test for vector variables with large sample sizes
K. Chalupka, P. Perona, and F. Eberhardt, 2018 · 2018
Cited alongside, same era.
Counterfactuals uncover the modular structure of deep generative models
M. Besserve, R. Sun, and B. Schölkopf, 2018b
Cited in the paper.
Z. C. Lipton, Y.-X. Wang, and A. Smola, 2018 · 2018
Later among the works it cites.
Deconfounding reinforcement learning in observational settings
C. Lu, B. Schölkopf, and J. M. Hernández-Lobato, 2018 · 2018
Later among the works it cites.
Failing loudly: An empirical study of methods for detecting dataset shift
S. Rabanser, S. Günnemann, and Z. C. Lipton, 2018 · 2018
Later among the works it cites.
The hardness of conditional independence testing and the generalised covariance measure
R. D. Shah and J. Peters, 2018 · 2018
Later among the works it cites.
Preventing failures due to dataset shift: Learning predictive models that transport
A. Subbaswamy, P. Schulam, and S. Saria, 2018 · 2018
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R. Suter, Đorđe Miladinović, B. Schölkopf, and S. Bauer, 2018 · 2018
Later among the works it cites.
March 2019
E. Brynjolfsson, A. Collis, W. E. Diewert, F. Eggers, and K. J. Fox · 2019
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