Related issues


Causality and prediction

  • K. Kiritoshi, T. Izumitani, K. Koyama, T. Okawachi, K. Asahara, and S. Shimizu. Estimating individual-level optimal causal interventions combining causal models and machine learning models. In Proc. KDD'21 Workshop on Causal Discovery, PMLR 150:55-77, 2021.
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  • T. Teshima, I. Sato, M. Sugiyama. Few-shot Domain Adaptation by Causal Mechanism Transfer. Proc. 37th International Conference on Machine Learning (ICML2020), 2020.
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  • K. Zhang, M. Gong, P. Stojanov, B. Huang, C. Glymour. Domain Adaptation As a Problem of Inference on Graphical Models. Arxiv preprint arXiv:2002.03278, 2020.
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  • A. Dhir, C. M. Lee. Integrating overlapping datasets using bivariate causal discovery. In Proc. 34-th AAAI Conference on Artificial Intelligence (AAAI2020), pp. xx--xx, New York, USA, 2020.
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  • T.-L. Nguyen, S. Kavuri, M. Lee. A multimodal convolutional neuro-fuzzy network for emotion understanding of movie clips. Neural Networks, xx: xx-xx, 2019.
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  • P. Blöbaum and S. Shimizu. Estimation of interventional effects of features on prediction. In Proc. 2017 IEEE International Workshop on Machine Learning for Signal Processing (MLSP2017), pp. xx--xx, Tokyo, Japan, 2017.
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  • H. Nyberg and P. Saikkonen. Forecasting with a noncausal VAR model. Computational Statistics & Data Analysis, 76: 536-555, 2013.
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  • M. Lanne, J. Luoto and P. Saikkonen. Optimal forecasting of noncausal autoregressive time series. International Journal of Forecasting, 28(3): 623-631, 2012.
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  • B. Schölkopf, D. Janzing, J. Peters, E. Sgouritsa, K. Zhang and J. Mooij. Semi-supervised learning in causal and anticausal settings. In Empirical Inference, pp. 129-141, 2013.
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  • B. Schölkopf, D. Janzing, J. Peters, E. Sgouritsa, K. Zhang and J. Mooij. On causal and anticausal learning. In Proc. 29th Int. Conf. on Machine Learning (ICML2012), pp. xx-xx, Edinburgh, Scotland, 2012.
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  • R. E. Tillman and P. Spirtes. When causality matters for prediction: investigating the practical tradeoffs. In JMLR Workshop and Conference Proceedings, Causality: Objectives and Assessment (Proc. NIPS2008 workshop on causality), 6: 137-146, 2010.
    [pdf] [videolecture] [Google scholar]

Testing, model fit and reliability

Evaluation of model assumptions

  • W. Wiedermann and X. Li. Confounder detection in linear mediation models: Performance of kernel-based tests of independence. Behavior Research Methods, xx: xx--xx, 2019.
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  • M. Guerini and A. Moneta. A method for agent-based models validation. LEM WORKING PAPER SERIES: 2016/16, 2016.
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  • D. Entner, P. O. Hoyer and P. Spirtes. Statistical test for consistent estimation of causal effects in linear non-Gaussian models. In JMLR Workshop and Conference Proceedings, AISTATS 2012 (Proc. 15th International Conference on Artificial Intelligence and Statistics), 22: 364-372, 2012.
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  • D. Entner and P. O. Hoyer. Discovering unconfounded causal relationships using linear non-Gaussian models. New Frontiers in Artificial Intelligence, Lecture Notes in Computer Science, 6797: 181-195, 2011.
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  • K. Ozaki, K. Nakamura and H. Murohashi. A multilevel model using 2nd and 3rd order moments. Proceedings of the Institute of Statistical Mathematics, 58(2): 207--221, 2010. (In Japanese)
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  • S. Shimizu and Y. Kano. Use of non-normality in structural equation modeling: Application to direction of causation. Journal of Statistical Planning and Inference, 138: 3483--3491, 2008.
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Statistical reliability

  • D. Strieder, T. Freidling, S. Haffner, and M. Drton. Confidence in Causal Discovery with Linear Causal Models. In Proc. 37th conference on Uncertainty in Artificial Intelligence (UAI 2021) pp.xx-xx, Online, 2021.
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  • W. Wiedermann, M. Hagmann and A. von Eye. Significance tests to determine the direction of effects in linear regression models. British Journal of Mathematical and Statistical Psychology, 68(1): 116--141, 2015.
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  • K. Thamvitayakul, S. Shimizu, T. Ueno, T. Washio and T. Tashiro. Bootstrap confidence intervals in DirectLiNGAM. In Proc. 2012 IEEE 12th International Conference on Data Mining Workshops (ICDMW2012), pp.659--668, Brussels, Belgium, 2012.
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  • Y. Komatsu, S. Shimizu and H. Shimodaira. Assessing statistical reliability of LiNGAM via multiscale bootstrap. In Proc. International Conference on Artificial Neural Networks (ICANN2010), pp.309-314, Thessaloniki, Greece, 2010.
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Learning from multiple datasets

  • Y. Zeng, S. Shimizu, R. Cai, F. Xie, M. Yamamoto, Z. Hao. Causal Discovery with Multi-Domain LiNGAM for Latent Factors. Arxiv preprint arXiv:2009.09176, 2020.
    [pdf] [Google scholar]

  • A. Dhir, C. M. Lee. Integrating overlapping datasets using bivariate causal discovery. In Proc. 34nd AAAI Conference on Artificial Intelligence (AAAI2020), pp. xx-xx, 2020.
    [pdf] [Google schlar]

  • B. Huang, K. Zhang, M. Gong, C. Glymour. Causal Discovery from Multiple Data Sets with Non-Identical Variable Sets. In Proc. 34th AAAI Conference on Artificial Intelligence (AAAI), pp. xx-xx, 2020.
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  • B. Huang, K. Zhang, P. Xie, M. Gong, E. P. Xing, C. Glymour. Specific and Shared Causal Relation Modeling and Mechanism-Based Clustering. In Advances in Neural Information Processing Systems 33 (NIPS2019), pp. xx-xx, 2019.
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  • V. R. López, L. E. S. Succar, F. O. Espina, L. E. Erro. Knowledge Transfer for Learning Subject-Specific Causal Probabilistic Graphical Models. Technical Report No. CCC-19-004, 2019.
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  • L. Xiang, S. Xie, P. McColgan, S. J. Tabrizi, R. I. Scahill, D. Zeng, and Y. Wang. Learning subject-specific directed acyclic graphs with mixed effects structural equation models from observational data. Frontiers in Genetics, 9: 430, 2018.
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  • K. Kadowaki, S. Shimizu and T. Washio. Estimation of causal structures in longitudinal data using non-Gaussianity. In Proc. 23rd IEEE International Workshop on Machine Learning for Signal Processing (MLSP2013), pp. 1--6, Southampton, United Kingdom, 2013.
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  • U. Schaechtle, K. Stathis and S. Bromuri. Multi-dimensional causal discovery. In Proc. 23rd International Joint Conference on Artificial Intelligence (IJCAI2013), pp. 1649--1655, Beijing, China, 2013.
    [pdf] [Google scholar]

  • S. Shimizu. Joint estimation of linear non-Gaussian acyclic models. Neurocomputing, 81: 104-107, 2012.
    [pdf] [Matlab code] [Python code by T. Ikeuchi and G. Haraoka] [Google scholar]

  • J. D. Ramsey, S. J. Hanson and C. 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.
    [pdf] [TETRAD IV] [Google scholar]

Others

  • D. Ibeling, T. Icard. A Topological Perspective on Causal Inference. Arxiv preprint arXiv:2107.08558, 2021.
    [pdf] [Google schlar]

  • X. Huang, F. Zhu, L. Holloway, A. Haidar. Causal Discovery from Incomplete Data using An Encoder and Reinforcement Learning. Arxiv preprint arXiv:2006.05554, 2020.
    [pdf] [Google schlar]

  • Y. Wang, V Menkovski, H Wang, X Du, M Pechenizkiy. Causal Discovery from Incomplete Data: A Deep Learning Approach. Arxiv preprint arXiv:2001.05343, 2020.
    [pdf] [Google schlar]

  • M. Peyrard, R. West. A Ladder of Causal Distances. ArXiv preprint arXiv:2005.02480, 2020.
    [pdf] [Google schlar]

  • X. Zheng, C. Dan, B. Aragam, P. Ravikumar, E. P. Xing. Learning Sparse Nonparametric DAGs. In Proc. 23rd International Conference on Artificial Intelligence and Statistics (AISTATS2020), Palermo, Sicily, Italy. PMLR: Volume xx..
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  • N. Fei and Y. Yang. An Integrated Causal Path Identification Method. Wuhan University Journal of Natural Sciences, 24(4): 305--313, 2019.
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  • R. Silva and S. Shimizu. Learning instrumental variables with structural and non-Gaussianity assumptions. Journal of Machine Learning Research, 18: 1--49, 2017.
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  • W. Wiedermann, E. C. Merkle, and A. von Eye. Direction of dependence in measurement error models. British Journal of Mathematical and Statistical Psychology, xx(xx): xx-xx, 2017.
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  • Y. Hong, Z. Hao, G. Mai, B. Chen, and R. Xie. Inferring causal direction from multi-dimensional causal networks for assessing harmful factors in security analysis. IEEE Access, xx(xx): xx--xx, 2017.
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  • R. Cai, Z. Zhang, Z. Hao, and M. Winslett. Sophisticated merging over random partitions: a scalable and robust causal discovery approach. IEEE Transactions on Neural Networks and Learning Systems, xx(xx): xx--xx, 2017.
    [pdf] [Google scholar]

  • G. Mai, S. Peng, Y. Hong, and P. Chen. Fast Causal Division for Supporting High Dimensional Causal Discovery. In Proc. 2017 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC), 2017 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC), xx: xx-xx, Guangzhou, China, 2017.
    [pdf] [Google scholar]

  • J, D. Ramsey, and D. Malinsky. Comparing the performance of graphical structure learning algorithms with TETRAD. Arxiv preprint arXiv:1607.08110, 2016.
    [pdf] [Google scholar]

  • R. Cai, Z. Zhang and Z. Hao. SADA: A general framework to support robust causation discovery. In JMLR Workshop and Conference Proceedings (Proc. 30th International Conference on Machine Learning, ICML2013), 28(2): 208-216, 2013.
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  • M. J. Kusner, Y. Sun, K. Sridharan, and K. Q. Weinberger. Private causal inference. In JMLR Workshop and Conference Proceedings, AISTATS 2016 (Proc. 19th International Conference on Artificial Intelligence and Statistics), 51: xx-xx, Cadiz, Spain, 2016.
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  • D. Entner, P. O. Hoyer and P. Spirtes. Statistical test for consistent estimation of causal effects in linear non-Gaussian models. In JMLR Workshop and Conference Proceedings, AISTATS 2012 (Proc. 15th International Conference on Artificial Intelligence and Statistics), 22: 364-372, 2012.
    [pdf] [supplementary] [code] [real data] [Google scholar]