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Notions of counterfactual invariance (CI) have proven essential for predictors that are fair, robust, and generalizable in the real world.
Capuchin: Causal database repair for algorithmic fairness
Salimi, B., Rodriguez, L., Howe, B., and Suciu, D. (2019) · 1902
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Arjovsky, M., Bottou, L., Gulrajani, I., and Lopez-Paz, D. (2019) · 1907
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Uci adult data set
Kohavi, R. and Becker, B. (1996) · 1996
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Vector integration and stochastic integration in Banach spaces
Dinculeanu, N. (2000) · 2000
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Causality: Models, Reasoning and Inference
Pearl, J. (2000) · 2000
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Learning with kernels: support vector machines, regularization, optimization, and beyond
Schölkopf, B., Smola, A. J., Bach, F., et al. (2002) · 2002
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Identifiability of path-specific effects
Avin, C., Shpitser, I., and Pearl, J. (2005) · 2005
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On the nystrom method for approximating a gram matrix for improved kernel-based learning
Drineas, P. and Mahoney, M. W. (2005) · 2005
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Measuring statistical dependence with hilbert-schmidt norms
Gretton, A., Bousquet, O., Smola, A. J., and Schölkopf, B. (2005) · 2005
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Optimal rates for the regularized least-squares algorithm
Caponnetto, A. and Vito, E. D. (2007) · 2007
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Hunting causes and using them: Approaches in philosophy and economics
Cartwright, N. (2007) · 2007
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Kernel measures of conditional dependence
Fukumizu, K., Gretton, A., Sun, X., and Schölkopf, B. (2007) · 2007
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Random features for large-scale kernel machines
Rahimi, A. and Recht, B. (2007) · 2007
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A hilbert space embedding for distributions
Smola, A., Gretton, A., Song, L., and Schölkopf, B. (2007) · 2007
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Effects of treatment on the treated: Identification and generalization
Shpitser, I. and Pearl, J. (2009) · 2009
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Hilbert space embeddings of conditional distributions with applications to dynamical systems
Song, L., Huang, J., Smola, A. J., and Fukumizu, K. (2009) · 2009
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On the validity of covariate adjustment for estimating causal effects
Shpitser, I., VanderWeele, T. J., and Robins, J. M. (2010) · 2010
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Reproducing kernel Hilbert spaces in probability and statistics
Berlinet, A. and Thomas-Agnan, C. (2011) · 2011
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Probability and stochastics
Çinlar, E. (2011) · 2011
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Conditional mean embeddings as regressors
Grünewälder, S., Lever, G., Gretton, A., Baldassarre, L., Patterson, S., and Pontil, M. (2012) · 2012
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E. (2012) · 2012
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Kernel bayes’ rule: Bayesian inference with positive definite kernels
Fukumizu, K., Song, L., and Gretton, A. (2013) · 2013
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Kernel embeddings of conditional distributions: A unified kernel framework for nonparametric inference in graphical models
Song, L., Fukumizu, K., and Gretton, A. (2013) · 2013
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Fast prediction for large-scale kernel machines
Hsieh, C.-J., Si, S., and Dhillon, I. S. (2014) · 2014
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Auto-encoding variational bayes
Kingma, D. P. and Welling, M. (2014) · 2014
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Big data’s disparate impact
Barocas, S. and Selbst, A. D. (2016) · 2016
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Group equivariant convolutional networks
Cohen, T. and Welling, M. (2016) · 2016
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Against counterfactual miracles
Dorr, C. (2016) · 2016
Cited alongside, same era.
Equality of opportunity in supervised learning
Hardt, M., Price, E., Price, E., and Srebro, N. (2016) · 2016
Cited alongside, same era.
Causal inference by using invariant prediction: identification and confidence intervals
Peters, J., Bühlmann, P., and Meinshausen, N. (2016) · 2016
Cited alongside, same era.
Random fourier features for kernel ridge regression: Approximation bounds and statistical guarantees
Avron, H., Kapralov, M., Musco, C., Musco, C., Velingker, A., and Zandieh, A. (2017) · 2017
Cited alongside, same era.
Invariance, causality and robustness
Bühlmann, P. (2020) · 2020
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A simple framework for contrastive learning of visual representations
Chen, T., Kornblith, S., Norouzi, M., and Hinton, G. (2020) · 2020
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A measure-theoretic approach to kernel conditional mean embeddings
Park, J. and Muandet, K. (2020) · 2020
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Metrizing weak convergence with maximum mean discrepancies
Simon-Gabriel, C.-J., Barp, A., Schölkopf, B., and Mackey, L. (2020) · 2020
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Unsupervised data augmentation for consistency training
Xie, Q., Dai, Z., Hovy, E., Luong, T., and Le, Q. (2020) · 2020
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Fairness with continuous optimal transport
Chiappa, S. and Pacchiano, A. (2021) · 2021
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Avoiding discrimination through causal reasoning
Kilbertus, N., Rojas Carulla, M., Parascandolo, G., Hardt, M., Janzing, D., and Schölkopf, B. (2017) · 2017
Cited alongside, same era.
Counterfactual fairness
Kusner, M. J., Loftus, J. R., Russell, C., and Silva, R. (2017) · 2017
Cited alongside, same era.
dsprites: Disentanglement testing sprites dataset
Matthey, L., Higgins, I., Hassabis, D., and Lerchner, A. (2017) · 2017
Cited alongside, same era.
Kernel mean embedding of distributions: A review and beyond
Muandet, K., Fukumizu, K., Sriperumbudur, B., and Schölkopf, B. (2017) · 2017
Cited alongside, same era.
Elements of causal inference: foundations and learning algorithms
Peters, J., Janzing, D., and Schölkopf, B. (2017) · 2017
Cited alongside, same era.
Memory efficient kernel approximation
Si, S., Hsieh, C.-J., and Dhillon, I. S. (2017) · 2017
Cited alongside, same era.
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Retiring adult: New datasets for fair machine learning
Ding, F., Hardt, M., Miller, J., and Schmidt, L. (2021) · 2021
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Fairness under feature exemptions: Counterfactual and observational measures
Dutta, S., Venkatesh, P., Mardziel, P., Datta, A., and Grover, P. (2021) · 2021
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Invariant causal representation learning for out-of-distribution generalization
Lu, C., Wu, Y., Hernández-Lobato, J. M., and Schölkopf, B. (2021) · 2021
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Algorithmic fairness: Choices, assumptions, and definitions
Mitchell, S., Potash, E., Barocas, S., D’Amour, A., and Lum, K. (2021) · 2021
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Counterfactual invariance to spurious correlations in text classification
Veitch, V., D’Amour, A., Yadlowsky, S., and Eisenstein, J. (2021) · 2021
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Causation with a human face: Normative theory and descriptive psychology
Woodward, J. (2021) · 2021
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On pearl’s hierarchy and the foundations of causal inference
Bareinboim, E., Correa, J. D., Ibeling, D., and Icard, T. (2022) · 2022
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The causal fairness field guide: Perspectives from social and formal sciences
Carey, A. N. and Wu, X. (2022) · 2022
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Selection, ignorability and challenges with causal fairness
Fawkes, J., Evans, R., and Sejdinovic, D. (2022) · 2022
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Causal feature selection for algorithmic fairness
Galhotra, S., Shanmugam, K., Sattigeri, P., and Varshney, K. R. (2022) · 2022
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Causally motivated shortcut removal using auxiliary labels
Makar, M., Packer, B., Moldovan, D., Blalock, D., Halpern, Y., and D’Amour, A. (2022) · 2022
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Asymmetry learning for counterfactually-invariant classification in OOD tasks
Mouli, S. C. and Ribeiro, B. (2022) · 2022
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Plecko, D. and Bareinboim, E. (2022) · 2022
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Efficient conditionally invariant representation learning
Pogodin, R., Deka, N., Li, Y., Sutherland, D. J., Veitch, V., and Gretton, A. (2022) · 2022
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A unifying causal framework for analyzing dataset shift-stable learning algorithms
Subbaswamy, A., Chen, B., and Saria, S. (2022) · 2022
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von Kügelgen, J., Mohamed, A., and Beckers, S. (2022) · 2022
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Breaking correlation shift via conditional invariant regularizer
Yi, M., Wang, R., Sun, J., Li, Z., and Ma, Z.-M. (2022) · 2022
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Results on counterfactual invariance
Fawkes, J. and Evans, R. J. (2023) · 2023
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Achieving non-discrimination in data release
Zhang, L., Wu, Y., and Wu, X. (2017) · 2023
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