Abstract
The cause-effect pair challenges focused on the development of inference methods to determine the causal relation between two variables. It is natural to then ask how such methods could generalize beyond the two variable case to settings that either involve more variables—such as is the case in graph learning—or to settings where the relationship between the candidate variables does not fall into one of the classes defined by the challenges. This chapter explores the extension of the proposed methods to such cases. It comes to the conclusion that such extensions are not likely to naturally evolve from the approaches that won the pair challenge.
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References
Tetrad code package. URL http://www.phil.cmu.edu/tetrad/about.html.
Peter Bühlmann, Jonas Peters, Jan Ernest, et al. Cam: Causal additive models, high-dimensional order search and penalized regression. The Annals of Statistics, 42(6):2526–2556, 2014.
K. Chalupka, P. Perona, and F. Eberhardt. Visual causal feature learning. In Proceedings of UAI, 2015.
K. Chalupka, P. Perona, and F. Eberhardt. Multi-level cause-effect systems. In Proceedings of AISTATS, 2016a.
Krzysztof Chalupka, Frederick Eberhardt, and Pietro Perona. Estimating causal direction and confounding of two discrete variables. arXiv preprint arXiv:1611.01504, 2016b.
Krzysztof Chalupka, Frederick Eberhardt, and Pietro Perona. Causal feature learning: an overview. Behaviormetrika, 44(1):137–164, 2017.
José AR Fonollosa. Conditional distribution variability measures for causality detection. arXiv preprint arXiv:1601.06680, 2016.
D. Geiger and J. Pearl. On the logic of causal models. In Proceedings of UAI, 1988.
Mingming Gong, Kun Zhang, Bernhard Schoelkopf, Dacheng Tao, and Philipp Geiger. Discovering temporal causal relations from subsampled data. In International Conference on Machine Learning, pages 1898–1906, 2015.
Olivier Goudet, Diviyan Kalainathan, Philippe Caillou, Isabelle Guyon, David Lopez-Paz, and Michele Sebag. Learning functional causal models with generative neural networks. In Explainable and Interpretable Models in Computer Vision and Machine Learning, pages 39–80. Springer, 2018.
Daniel Hernandez-Lobato, Pablo Morales Mombiela, David Lopez-Paz, and Alberto Suarez. Non-linear causal inference using gaussianity measures. Journal of Machine Learning Research, 2016.
P.O. Hoyer, D. Janzing, J.M. Mooij, J.R. Peters, and B. Schölkopf. Nonlinear causal discovery with additive noise models. In D. Koller, D. Schuurmans, Y. Bengio, and L. Bottou, editors, Advances in Neural Information Processing Systems 21, pages 689–696. 2008a.
P.O. Hoyer, S. Shimizu, A.J. Kerminen, and M. Palviainen. Estimation of causal effects using linear non-Gaussian causal models with hidden variables. International Journal of Approximate Reasoning, 49:362–378, 2008b.
Antti Hyttinen, Sergey Plis, Matti Järvisalo, Frederick Eberhardt, and David Danks. A constraint optimization approach to causal discovery from subsampled time series data. International Journal of Approximate Reasoning, 90:208–225, 2017.
Dominik Janzing and Bernhard Schoelkopf. Detecting confounding in multivariate linear models via spectral analysis. arXiv preprint arXiv:1704.01430, 2017.
Dominik Janzing, Jonas Peters, Joris Mooij, and Bernhard Schölkopf. Identifying confounders using additive noise models. In Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pages 249–257. AUAI Press, 2009.
G Lacerda, P Spirtes, J Ramsey, and P. O. Hoyer. Discovering cyclic causal models by independent components analysis. In Proceedings of UAI, pages 366–374, 2008.
David Lopez-Paz, Krikamol Muandet, Bernhard Schölkopf, and Ilya O. Tolstikhin. Towards a learning theory of cause-effect inference. In ICML, pages 1452–1461, 2015.
Marc Maier. Causal Discovery for Relational Domains: Representation, Reasoning, and Learning. PhD thesis, 2014.
Marc Maier, Katerina Marazopoulou, David Arbour, and David Jensen. A sound and complete algorithm for learning causal models from relational data. arXiv preprint arXiv:1309.6843, 2013.
C. Meek. Strong completeness and faithfulness in bayesian networks. In Proceedings of UAI, pages 411–418. Morgan Kaufmann Publishers Inc., 1995.
Bram Minnaert. Feature importance in causal inference for numerical and categorical variables.
Diogo Moitinho de Almeida. Pattern-based causal feature extraction.
Joris M Mooij, Jonas Peters, Dominik Janzing, Jakob Zscheischler, and Bernhard Schölkopf. Distinguishing cause from effect using observational data: methods and benchmarks. The Journal of Machine Learning Research, 17(1):1103–1204, 2016.
Jonas Peters, Joris M Mooij, Dominik Janzing, and Bernhard Schölkopf. Causal discovery with continuous additive noise models. The Journal of Machine Learning Research, 15(1):2009–2053, 2014.
Jonas Peters, Dominik Janzing, and Bernhard Schölkopf. Elements of causal inference: foundations and learning algorithms. MIT press, 2017.
Joseph D Ramsey, Stephen José Hanson, and Clark Glymour. Multi-subject search correctly identifies causal connections and most causal directions in the dcm models of the smith et al. simulation study. NeuroImage, 58(3):838–848, 2011.
Subhradeep Roy and Benjamin Jantzen. Detecting causality using symmetry transformations. Chaos: An Interdisciplinary Journal of Nonlinear Science, 28(7):075305, 2018.
Spyridon Samothrakis, Diego Perez, and Simon Lucas. Training gradient boosting machines using curve-fitting and information-theoretic features for causal direction detection.
Oliver Schulte and Hassan Khosravi. Learning graphical models for relational data via lattice search. Machine Learning, 88(3):331–368, 2012.
Shohei Shimizu and Kenneth Bollen. Bayesian estimation of causal direction in acyclic structural equation models with individual-specific confounder variables and non-gaussian distributions. The Journal of Machine Learning Research, 15(1):2629–2652, 2014.
Shohei Shimizu, Patrik O Hoyer, Aapo Hyvärinen, and Antti Kerminen. A linear non-gaussian acyclic model for causal discovery. Journal of Machine Learning Research, 7(Oct):2003–2030, 2006.
P. Spirtes, C. Glymour, and R. Scheines. Causation, Prediction and Search. MIT Press, 2 edition, 2000.
K. Zhang and A. Hyvärinen. On the identifiability of the post-nonlinear causal model. In Proceedings of UAI, pages 647–655. AUAI Press, 2009.
Kun Zhang and Lai-Wan Chan. Extensions of ICA for causality discovery in the Hong Kong stock market. In International Conference on Neural Information Processing, pages 400–409. Springer, 2006.
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Eberhardt, F. (2019). Beyond Cause-Effect Pairs. In: Guyon, I., Statnikov, A., Batu, B. (eds) Cause Effect Pairs in Machine Learning. The Springer Series on Challenges in Machine Learning. Springer, Cham. https://doi.org/10.1007/978-3-030-21810-2_6
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