Extensions

Nonlinear models

Basic model with no latent confounders

  • R. Cai, W. Chen, J. Qiao, Z. Hao. On the Role of Entropy-based Loss for Learning Causal Structures with Continuous Optimization. Arxiv preprint arXiv:2104.05441, 2021.
    [pdf] [Google scholar]

  • I. Khemakhem, R. P. Monti, R. Leech, A. Hyvärinen. Causal Autoregressive Flows. arXiv:2011.02268, 2020.
    [pdf] [Google schlar]

  • R. P. Monti, I. Khemakhem, A. Hyvarinen. Autoregressive flow-based causal discovery and inference. arXiv:2007.09390, 2020.
    [pdf] [Google schlar]

  • P. Wu, K. Fukumizu. Causal Mosaic: Cause-Effect Inference via Nonlinear ICA and Ensemble Method. JMLR Workshop and Conference Proceedings, AISTATS 2020 (Proc. 23th International Conference on Artificial Intelligence and Statistics), x: xx-xx, 2020.
    [pdf] [Google scholar]

  • D. Chicharro, S. Panzeri, I. Shpitser. Conditionally-additive-noise Models for Structure Learning. Arxiv preprint arXiv:1905.08360, 2019. [pdf] [Google scholar]

  • C. Klüppelberg, and S. Lauritzen. Bayesian Networks for Max-linear Models. Arxiv preprint arXiv:1901.03948, 2019.
    [pdf] [Google scholar]

  • I. Ng, Z. Fang, S. Zhu, Z. Chen. Masked Gradient-Based Causal Structure Learning. Arxiv preprint arXiv:1910.08527, 2019.
    [pdf] [Google scholar]

  • R. Pio Monti, K. Zhang, A. Hyvärinen. Causal Discovery with General Non-Linear Relationships Using Non-Linear ICA. Proc. 35th Conf. on Uncertainty in Artificial Intelligence (UAI2019), pp. xx--xx, Tel Aviv, Israel, 2019.
    [pdf] [Google scholar]

  • R. Cai, J. Qiao, K. Zhang, Z. Zhang, Z. Hao. Causal Discovery with Cascade Nonlinear Additive Noise Models. In Proc. 28th International Joint Conference on Artificial Intelligence (IJCAI 2019), pp. xx--xx, Macao, China, 2019.
    [pdf] [Google scholar]

  • M. Rojas-Carulla, M. Baroni, and D. Lopez-Paz. Causal discovery using proxy variables. Arxiv preprint arXiv:1702.07306, 2017.
    [pdf] [Google scholar]

  • J. Peters. On the intersection property of conditional independence and its application to causal discovery. Journal of Causal Inference, 3(1): 97--108, 2014.
    [pdf] [Google scholar]

  • J. Peters, J. Mooij, D. Janzing and B. Schölkopf. Causal discovery with continuous additive noise models. Journal of Machine Learning Research, 15: 2009--2053, 2014.
    [pdf] [Google scholar]

  • J. Peters, J. Mooij, D. Janzing and B. Schölkopf. Identifiability of causal graphs using functional models. In Proc. 27th Conf. on Uncertainty in Artificial Intelligence (UAI2011), pp. 589--598, Barcelona, Spain, 2011.
    [pdf] [code] [Google scholar]

  • R. E. Tillman, A. Gretton and P. Spirtes. Nonlinear directed acyclic structure learning with weakly additive noise models. In Advances in Neural Information Processing Systems 22 (NIPS2009), pp. 1847-1855, 2010.
    [pdf] [Google scholar]

  • K. Zhang and A. Hyvärinen. On the identifiability of the post-nonlinear causal model. In Proc. 25th Conf. on Uncertainty in Artificial Intelligence (UAI2009), pp. 647-655, Montreal, Canada, 2009.
    [pdf] [code] [Google scholar]

  • K. Zhang and A. Hyvärinen. Distinguishing causes from effects using nonlinear acyclic causal models. In JMLR Workshop and Conference Proceedings, Causality: Objectives and Assessment (Proc. NIPS2008 workshop on causality), 6: 157-164, 2010.
    [pdf] [videolecture] [code] [Google scholar]

  • J. Mooij and D. Janzing. Distinguishing between cause and effect. In JMLR Workshop and Conference Proceedings, Causality: Objectives and Assessment (Proc. NIPS2008 workshop on causality), 6: 147-156, 2010.
    [pdf] [Google scholar]

  • P. O. Hoyer, D. Janzing, J. Mooij, J. Peters and B. Schölkopf. Nonlinear causal discovery with additive noise models. In Advances in Neural Information Processing Systems 21 (NIPS2008), pp. 689-696, 2009.
    [pdf] [code] [Google scholar]

  • K. Zhang and L. Chan. Minimal nonlinear distortion principle for nonlinear independent component analysis. Journal of Machine Learning Research, 9: 2455--2487, 2008.
    [pdf] [Google scholar]

Estimation of basic nonlinear models with no latent confounders

  • NEW B. Kap, M. Aleksandrova, T. Engel. Causal Identification with Additive Noise Models: Quantifying the Effect of Noise. Arxiv preprint arXiv:2108.08871, 2021.
    [pdf] [Google scholar]

  • M. E. Jakobsen, R. D. Shah, P. Bühlmann, J. Peters. Structure Learning for Directed Trees. Arxiv preprint arXiv:2108.08871, 2021.
    [pdf] [Google scholar]

  • R. Tu, K. Zhang, H. Kjellström, C. Zhang. Optimal transport for causal discovery. Workshop on the Neglected Assumptions in Causal Inference (NACI) at the 38-th International Conference on Machine Learning, 2021.
    [pdf] [Google scholar]

  • Y. He, P. Cui, Z. Shen, R. Xu, F. Liu, Y. Jiang. DARING: Differentiable Causal Discovery with Residual Independence. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD2021), 2021.
    [pdf] [Google scholar]

  • J. Yang, G. Fan, K. Xie, Q. Chen, A. Wang. Additive noise model structure learning based on rank correlation. Information Sciences, 571: 499-526, 2021.
    [pdf] [Google scholar]

  • X. Wang, Y. Du, S. Zhu, L. Ke, Z. Chen, J. Hao, J. Wang . Ordering-Based Causal Discovery with Reinforcement Learning. Arxiv preprint arXiv:2105.0663.05691, 2020.
    [pdf] [Google schlar]

  • H. Wu and M. D. Wang. An Information Theoretic Learning for Causal Direction Identification. In Proc. 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC), pp. xx--xx, Madrid, Spain, 2020.
    [pdf] [Google scholar]

  • Z. Fang, S. Zhu, J. Zhang, Y. Liu, Z. Chen, Y. He. Low Rank Directed Acyclic Graphs and Causal Structure Learning. Arxiv preprint arXiv:2006.05691, 2020.
    [pdf] [Google schlar]

  • K. Uemura and S. Shimizu. Estimation of post-nonlinear causal models using autoencoding structure. In Proc. 45th International Conference on Acoustics, Speech, and Signal Processing (ICASSP2020), pp. xx--xx, Barcelona, Spain, 2020.
    [pdf] [Google scholar]

  • M. Kurthen, T. Enßlin. A Bayesian Model for Bivariate Causal Inference. Entropy, 22(46): xx−xx, 2020.
    [pdf] [Google scholar]

  • B. Wang, Q. Zhou. Causal network learning with non-invertible functional relationships. Arxiv preprint arXiv: 2004.09646, 2020.
    [pdf] [Google schlar]

  • M. Yang, F. Liu, Z. Chen, X. Shen, J. Hao, J. Wang. CausalVAE: Structured Causal Disentanglement in Variational Autoencoder. Arxiv preprint arXiv:2004.08697, 2020.
    [pdf] [Google schlar]

  • K. Assaad, E. Devijver, E. Gaussier, A. Ait-Bachir. Scaling Causal Inference in Additive Noise Models. In Proc. 2019 ACM SIGKDD Workshop on causal discovery (CD2019), pp. xx-xx, Anchorage, Alaska, 2019.
    [pdf] [Google scholar]

  • M. R. Heydari, S. Salehkaleybar, K. Zhang. Adversarial Orthogonal Regression: Two non-Linear Regressions for Causal Inference. Arxiv preprint arXiv:1909.04454, 2019.
    [pdf] [Google scholar]

  • I. Ng, S. Zhu, Z. Chen, Z. Fang. A Graph Autoencoder Approach to Causal Structure Learning. Arxiv preprint arXiv:1911.07420, 2019.
    [pdf] [Google schlar]

  • H. Sasaki, T. Takenouchi, R. Monti, A. Hyvärinen. Robust contrastive learning and nonlinear ICA in the presence of outliers. Arxiv preprint arXiv:1911.00265, 2019.
    [pdf] [Google schlar]

  • Y. Hong, Z. Hao, G. Mai, H. Huang, A. K. Sangaiah. Causal Discovery Combining K2 with Brain Storm Optimization Algorithm. Molecules, xx(xx): xx−xx, 2018.
    [pdf] [Google scholar]

  • D. Kalainathan, O. Goudet, I. Guyon, D. Lopez-Paz, and M. Sebag. SAM: structural agnostic model, causal discovery and penalized adversarial learning. Arxiv preprint arXiv:1803.04929, 2018.
    [pdf] [Google scholar]

  • O. Goudet, D. Kalainathan, P. Caillou, I. Guyon, D. Lopez-Paz, and M. Sebag. Causal generative neural networks. Arxiv preprint arXiv:1711.08936, 2017.
    [pdf] [Google scholar]

  • O. Goudet, D. Kalainathan, P. Caillou, D. Lopez-Paz, I. Guyon, M. Sebag, A. Tritas, and P. Tubaro. Learning functional causal models with generative neural networks. Arxiv preprint arXiv:1709.05321, 2017.
    [pdf] [Google scholar]

  • A. Pérez-Suay and G. Camps-Valls. Causal inference in geosciences with kernel sensitivity maps. In Proc. 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS2017), pp. 478-486, Fort Worth, TX, USA, 2017.
    [pdf] [Google scholar]

  • J. Song, S. Oyama, and M. Kurihara. Tell cause from effect: models and evaluation. International Journal of Data Science and Analytics, xx(xx): xx−xx, 2017.
    [pdf] [Google scholar]

  • Y. Hong, Z. Liu, and G. Mai. An efficient algorithm for large-scale causal discovery. Soft Computing, xx(xx): xx−xx, 2016.
    [pdf] [Google scholar]

  • J. M. Mooij, J. Peters, D. Janzing, J. Zscheischler, and B. Schölkopf. Distinguishing cause from effect using observational data: methods and benchmarks. Journal of Machine Learning Research, 17(32): 1−102, 2016.
    [pdf] [appendix] [Google scholar]

  • D. Hernández-Lobato, P. Morales-Mombiela, D. Lopez-Paz, and A. Suárez. Non-linear causal inference using Gaussianity measures. Journal of Machine Learning Research, 17(28): 1−39, 2016.
    [pdf] [appendix] [Google scholar]

  • B. Schölkopf, K. Muandet, K. Fukumizu, and J. Peters. Computing functions of random variables via reproducing kernel Hilbert space representations. Statistics and Computing, 25(4): 755-766, 2015.
    [pdf] [Google scholar]

  • S. R. Flaxman, D. B. Neill, and A. J. Smola . Gaussian processes for independence tests with non-iid data in causal inference. ACM Transactions on Intelligent Systems and Technology, xx(xx): xx-xx, 201x (Special Issue on Causal Discovery and Inference). In press.
    [pdf] [Google scholar]

  • K. Zhang, Z. Wang, J. Zhang, and B. Schölkopf. On estimation of functional causal models: General results and application to post-nonlinear causal model. ACM Transactions on Intelligent Systems and Technology, xx(xx): xx-xx, 201x (Special Issue on Causal Discovery and Inference). In press.
    [pdf] [Google scholar]

  • P. Bühlmann, J. Peters and J. Ernest. CAM: Causal Additive Models, high-dimensional order search and penalized regression. Annals of Statistics, 42(6): 2526-2556, 2014.
    [pdf] [Google scholar]

  • S. Kpotufe, E. Sgouritsa, D. Janzing and B. Schölkopf. Consistency of causal Inference under the additive noise model. In Proc. 31st Int. Conf. on Machine Learning (ICML2014), pp. 478-486, Beijing, China, 2014.
    [pdf] [Google scholar]

  • K. Zhang, Z. Wang and B. Schölkopf. On estimation of functional causal models: Post-nonlinear causal model as an example. In Proc. 2013 IEEE 13rd International Conference on Data Mining Workshops (ICDMW2013), pp. 139-146, 2013.
    [pdf] [Google scholar]

  • Z. Hao, J. Huang, R. Cai and W. Wen. A hybrid approach for large scale causality discovery. Emerging Intelligent Computing Technology and Applications: 1-6, 2013.
    [pdf] [Google scholar]

  • A. Hyvärinen and S. M. Smith. Pairwise likelihood ratios for estimation of non-Gaussian structural equation models. Journal of Machine Learning Research, 14(Jan): 111--152, 2013.
    [pdf] [Matlab code] [Google scholar]

  • C. Nowzohour and P. Bühlmann. Score-based causal learning in additive noise models. Arxiv preprint arXiv:1311.6359, 2013.
    [pdf] [Google scholar]

  • R. Henao and O. Winther. Sparse linear identifiable multivariate modeling. Journal of Machine Learning Research, 12(Mar): 863--905, 2011.
    [pdf] [code] [Google scholar]

  • F. Jiang, G. Gao and H. Zhu. Two-stage identification for nonlinear causal relationships. In Proc. Sixth International Conference on Natural Computation (ICNC2010), pp. 4390-4394, 2010.
    [pdf] [Google scholar]

  • M. Yamada, M. Sugiyama and J. Sese. Least-squares independence regression for non-linear causal inference under non-Gaussian noise. Machine Learning, 96(3): 249--267, 2014.
    [pdf] [Google scholar]

  • M. Yamada and M. Sugiyama. Dependence minimizing regression with model selection for non-linear causal inference under non-Gaussian noise. In Proc. 24th AAAI Conference on Artificial Intelligence (AAAI-10), pp. 643-648, Atlanta, Gergia, USA, 2010.
    [pdf] [Google scholar]

  • J. Mooij, O. Stegle, D. Janzing, K. Zhang and B. Schölkopf. Probabilistic latent variable models for distinguishing between cause and effect. In Advances in Neural Information Processing Systems 23 (NIPS2010), pp. 1687-1695, 2010.
    [pdf] [code] [Google scholar]

  • K. Zhang and A. Hyvärinen Causality discovery with additive disturbances: an information-theoretical perspective. In Proc. European Conference on Machine Learning (ECML2009), Bled, Slovenia, pp. 570-585, 2009.
    [pdf] [Google scholar]

  • J. Mooij, D. Janzing, J. Peters and B. Schölkopf. Regression by dependence minimization and its application to causal inference in additive noise models. In Proc. 26th Int. Conf. on Machine Learning (ICML2009), pp. 745-752, Montreal, Canada, 2009.
    [pdf] [real data] [Google scholar]

  • Y. Tamada, S. Imoto and S. Miyano. Parallel algorithm for learning optimal Bayesian network structure. Journal of Machine Learning Research, 12: 2437–2459, 2011.
    [pdf] [software] [Google scholar]

  • S. Imoto, S. Kim, T. Goto, S. Aburatani, K. Tashiro, S. Kuhara and S. Miyano. Bayesian network and nonparametric heteroscedastic regression for nonlinear modeling of genetic network. Proc. 1st IEEE Computer Society Bioinformatics Conference, 219-227, 2002.
    [pdf] [Google scholar]

  • S. Imoto, T. Goto and S. Miyano. Estimation of genetic networks and functional structures between genes by using Bayesian networks and nonparametric regression. Proc. Pacific Symposium on Biocomputing 2002, 175-186, 2002.
    [pdf] [software] [Google scholar]

Others

  • T. N. Maeda and S. Shimizu. Causal additive models with unobserved variables. In Proc. 37th Conf. on Uncertainty in Artificial Intelligence (UAI2021), pages xx–xx, Online, 2021.
    [pdf] [Google scholar]

  • K. Assaad, E. Devijver, E. Gaussier, and A. Ait-Bachir. A Mixed Noise and Constraint-based Approach to Causal Inference in Time Series. In Proc. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD) 2021, VIRTUAL. 2021.
    [pdf] [Google scholar]

  • C. Breunig, P. Burauel. Testability of Reverse Causality without Exogeneous Variation. Arxiv preprint arXiv:2107.05936, 2021.
    [pdf] [Google schlar]

  • M. Nagase and Y. Kano. Cyclic structural causal model with unobserved confounder effect. Communications in Statistics - Theory and Methods, xx: xx-xx. 2021.
    [pdf] [Google scholar]

  • V. P. Nia, X. Li, M. Asgharian, S. Hu, Z. Chen, Y. Geng. Clustering Causal Additive Noise Models. Arxiv preprint arXiv:2006.04877, 2020.
    [pdf] [Google schlar]

  • C. M. Lee, C. Hart, J. G. Richens, S. Johri. Leveraging directed causal discovery to detect latent common causes. Arxiv preprint arXiv:1910.10174, 2019.
    [pdf] [Google schlar]

  • B. Zhao and G. Luo. A New Causal Direction Reasoning Method for Decision Making on Noisy Data. In Proc. 2019 IEEE Intelligent Vehicles Symposium (IV) Paris, France. 2019.
    [pdf] [Google scholar]

  • S. Hu, Z. Chen, V. P. Nia, L. Chan, and Y. Geng. Causal Inference and Mechanism Clustering of a Mixture of Additive Noise Models. In Advances in Neural Information Processing Systems 32 (NIPS2018), pp. xx-xx, 2018.
    [pdf] [Google scholar]

  • A. Mateo-Sanchis, J. Muñoz-Marí, A. Pérez-Suay, and G. Camps-Valls. Warped Gaussian Processes in Remote Sensing Parameter Estimation and Causal Inference. IEEE Geoscience and Remote Sensing Letters, xx: xx-xx, 2018.
    [pdf] [Google scholar]

  • R. P. Monti, K. Zhang, A. Hyvärinen. NonSENS: Non-Linear SEM Estimation using Non-Stationarity. NuerIPS Causal Learning Workshop, 2018.
    [pdf] [Google scholar]

  • J. M. Peña. Learning causal AMP chain graphs. In Proc. 3rd International Workshop on Advanced Methodologies for Bayesian Networks, PMLR 73:33-44, 2017.
    [pdf] [Google scholar]

  • M. Nagase and Y. Kano. Cyclic structural equation models and their identifiability. In Proc. 2nd ISI Regional Statistics Conference, Bali, Indonesia, 2017.
    [pdf] [Google scholar]

  • P. K. Parida, T. Marwalaa, and S. Chakraverty. Altered-LiNGAM (ALiNGAM) for solving nonlinear causal models when data is nonlinear and noisy. Communications in Nonlinear Science and Numerical Simulation, 52: 190–202, 2017.
    [pdf] [Google scholar]

  • K. Zhang, J. Zhang, B. Huang, B. Schölkopf, and C. Glymour. On the identifiability and estimation of functional causal models in the presence of outcome-dependent selection. In Proc. 32nd Conf. on Uncertainty in Artificial Intelligence (UAI2016), New York City, NY, USA, 2016.
    [pdf] [Google scholar]

  • B. Schölkopf, D. W. Hogg, D. Wang, D. Foreman-Mackey, D. Janzing, C.-J. Simon-Gabriel, and J. Peters. Modeling confounding by half-sibling regression. Proceedings of the National Academy of Sciences, 113(27): 7391–7398, 2016.[pdf] [Google scholar]

  • B. Huang, K. Zhang, and B. Schölkopf. Identification of time-dependent causal model: a Gaussian process treatment. In Proc. 24th International Joint Conference on Artificial Intelligence (IJCAI2015), pp. xx-xx, Buenos Aires, Argentina, 2015.
    [pdf] [Google scholar]

  • J. Peters, D. Janzing and B. Schölkopf. Causal inference on time series using restricted structural equation models. In Advances in Neural Information Processing Systems 26 (NIPS2013), pp. 154-162, 2013.
    [pdf] [Google scholar]

  • E. Sgouritsa, D. Janzing, J. Peters and B. Schölkopf. Identifying finite mixtures of nonparametric product distributions and causal Inference of confounders. In Proc. 29th Conf. on Uncertainty in Artificial Intelligence (UAI2013), Bellevue, Washington, USA, 2013.
    [pdf] [code] [Google scholar]

  • J. Mooij, D. Janzing, T. Heskes and B. Schölkopf. Causal discovery with cyclic additive noise models. In Advances in Neural Information Processing Systems 24 (NIPS2011), pp. xx-xx, 2011.
    [pdf] [code] [Google scholar]

  • J. Hirayama and A. Hyvärinen. Structural equations and divisive normalization for energy-dependent component analysis. In Advances in Neural Information Processing Systems 24 (NIPS2011), pp. xx-xx, 2011.
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  • K. Zhang, B. Schölkopf and D. Janzing. Invariant Gaussian process latent variable models and application in causal discovery. In Proc. 26th Conf. on Uncertainty in Artificial Intelligence (UAI2010), Catalina Island, California, USA, 2010.
    [pdf] [code] [Google scholar]

  • D. Janzing, J. Peters, J. Mooij and B. Schölkopf. Identifying confounders using additive noise models. In Proc. 25th Conf. on Uncertainty in Artificial Intelligence (UAI2009), pp. 249-257, Montreal, Canada, 2009.
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  • S. Kim, S. Imoto and S. Miyano. Dynamic Bayesian network and nonparametric regression for nonlinear modeling of gene networks from time series gene expression data. In Computational Methods in Systems Biology (Proc. CMSB2003), pp. 104-113, 2003.[pdf] [Google scholar]

Discrete variables

  • NEW W. Wei and L. Feng. Nonlinear Causal Structure Learning for Mixed Data. In Proc. 2021 IEEE International Conference on Data Mining (ICDM2021), pp. xx-xx, 2021.
    [pdf] [Google scholar]

  • J. Suzuki and Y. Inaoka. Causal order identification to address confounding: binary variables. Behaviormetrika, xx: xx-xx, 2021.
    [pdf] [Google scholar]

  • H. Zhang, C. Yan, S. Zhou, J. Guan, J. Zhang. Combined cause inference: Definition, model and performance. Information Sciences,574: 431-443, 2021.
    [pdf] [Google scholar]

  • X. Liu, Z. Xu, P. Guo. Causal Inference for Mixed-Type Data in Additive Noise Models. In Proc. 27th Int. Conf. on Neural Information Processing (ICONIP2020), pp. 223-234, 2020.
    [pdf] [Google scholar]

  • S. Compton, M. Kocaoglu, K. Greenewald, D. Katz. Entropic Causal Inference: Identifiability and Finite Sample Results. In Advances in Neural Information Processing Systems 34 (NeurIPS2020), pp. xx-xx, 2020.
    [pdf] [Google scholar]

  • J. Choi, R. Chapkin, Y. Ni. Bayesian Causal Structural Learning with Zero-Inflated Poisson Bayesian Networks. In Advances in Neural Information Processing Systems 34 (NeurIPS2020), pp. xx-xx, 2020.
    [pdf] [Google scholar]

  • M. Yamayoshi, J. Tsuchida, H. Yadohisa. An estimation of causal structure based on Latent LiNGAM for mixed data. Behaviormetrika, xx: xx-xx, 2019.
    [pdf] [Google scholar]

  • G. Park and S. Park. High-Dimensional Poisson Structural Equation Model Learning via l1-Regularized Regression. Journal of Machine Learning Research, 20: xx-xx, 2019.
    [pdf] [Google scholar]

  • G. Park and H. Park. Identifiability of Generalized Hypergeometric Distribution (GHD) Directed Acyclic Graphical Models. In Proc. 22nd International Conference on Artificial Intelligence and Statistics (AISTATS) 2019, Naha, Okinawa, Japan. PMLR: Volume 89.
    [pdf] [Google scholar]

  • K. Budhathoki and J. Vreeken. Accurate Causal Inference on Discrete Data. xx, 2018.
    [pdf] [Google scholar]

  • R. Cai, J. Qiao, K. Zhang, Z. Zhang, Z. Hao. Causal Discovery on Discrete Data using Hidden Compact Representation. In Advances in Neural Information Processing Systems 28 (NIPS2018), pp. xx-xx, 2018.
    [pdf] [Google scholar]

  • C. Li and S. Shimizu. Combining linear non-Gaussian acyclic model with logistic regression model for estimating causal structure from mixed continuous and discrete Data. Arxiv preprint arXiv:1802.05889, 2018.
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  • W. Wenjuan, F. Lu, and L. Chunchen. Mixed Causal Structure Discovery with Application to Prescriptive Pricing. In Proc. 27th International Joint Conference on Artificial Intelligence (IJCAI2018), pp. xx--xx, Stockholm, Sweden, 2018.
    [pdf] [Google scholar]

  • G. Park and G. Raskutti. Learning quadratic variance function (QVF) DAG models via OverDispersion Scoring (ODS). Journal of Machine Learning Research, 18: xx-xx, 2018.
    [pdf] [Google scholar]

  • G. Park. Learning generalized hypergeometric distribution (GHD) DAG models. Arxiv preprint arXiv:1805.02848, 2018.
    [pdf] [Google scholar]

  • A. von Eye and W. Wiedermann. Direction of effects in categorical variables: looking inside the table. Journal for Person-Oriented Research , 3(1): 11-27, 2017.
    [pdf] [Google scholar]

  • M. Kocaoglu, A. G. Dimakis, S. Vishwanath, and B. Hassibi. Entropic causality and greedy minimum entropy coupling. In Proc. 2017 IEEE International Symposium on Information Theory (ISIT2017), pp. xx--xx, Aachen, Germany, 2017.
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  • M. Kocaoglu, A. G. Dimakis, S. Vishwanath, and B. Hassibi. Entropic causal inference. In Proc. 31st AAAI Conference on Artificial Intelligence (AAAI2017), pp. 1156--1162, San Francisco, California, USA, 2017.
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  • C. M. Lee and R. W. Spekkens. Causal inference via algebraic geometry: Feasibility tests for functional causal structures with two binary observed variables. Journal of Causal Inference, xx(xx): xx-xx, 2017.
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  • G. Park and G. Raskutti. Learning large-scale Poisson DAG models based on overdispersion scoring. In Advances in Neural Information Processing Systems 28 (NIPS2015), pp. xx-xx, 2015.
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  • W. Chen, Z. Hao, R. Cai, X. Zhang, Y. Hu, and M. Liu. Multiple-cause discovery combined with structure learning for high-dimensional discrete data and application to stock prediction. Soft Computing, xx(xx): xx--xx, 2015. In press.
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  • C. M. Lee and R. W. Spekkens. Causal inference via algebraic geometry: necessary and sufficient conditions for the feasibility of discrete causal models. Arxiv preprint arXiv:1506.03880, 2015.
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  • F. Liu and L. Chan. Causal discovery on discrete data with extensions to mixture model. ACM Transactions on Intelligent Systems and Technology, xx(xx): xx--xx, 201x (Special Issue on Causal Discovery and Inference). In press.
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  • J. Suzuki, T. Inazumi, T. Washio and S. Shimizu. Identifiability of an integer modular acyclic additive noise model and its causal structure discovery. Arxiv preprint arXiv:1401.5625, 2014.
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  • T. Inazumi, T. Washio, S. Shimizu, J. Suzuki, A. Yamamoto and Y. Kawahara. Causal discovery in a binary exclusive-or skew acyclic model: BExSAM. Arxiv preprint arXiv:1401.5636, 2014.
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  • T. Inazumi, T. Washio, S. Shimizu, J. Suzuki, A. Yamamoto and Y. Kawahara. Discovering causal structures in binary exclusive-or skew acyclic models. In Proc. 27th Conf. on Uncertainty in Artificial Intelligence (UAI2011), pp. 373--382, Barcelona, Spain, 2011.
    [pdf] [Matlab code] [Google scholar]

  • J. Peters, D. Janzing and B. Schölkopf. Causal inference on discrete data using additive noise models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(12): 2436--2450, 2011.
    [pdf] [Google scholar]

  • J. Peters, D. Janzing and B. Schölkopf. Identifying cause and effect on discrete data using additive noise models. In JMLR Workshop and Conference Proceedings, AISTATS 2010 (Proc. 13th International Conference on Artificial Intelligence and Statistics), 9: 597-604, 2010.
    [pdf] [code] [Google scholar]