Extensions

Nonlinear models

Basic model with no latent confounders

• A. Dallakyan, Y. Ni. Generalized Criterion for Identifiability of Additive Noise Models Using Majorization. arXiv preprint arXiv:2404.05148, 2024.
  [pdf] [Google scholar]

• O. Faruque, S. Ali, X. Zheng, J. Wang. TS-CausalNN: Learning Temporal Causal Relations from Non-linear Non-stationary Time Series Data. arXiv:2404.01466, 2024.
  [pdf] [Google scholar]

• W. Liu, B. Huang, E. Gao, Q. Ke, H. Bondell, M. Gong. Causal Discovery with Mixed Linear and Nonlinear Additive Noise Models: A Scalable Approach. In Proc. 3rd Conference on Causal Learning and Reasoning, PMLR 236:1237-1263, 2024.
  [pdf] [Google scholar]

• N. Yin, T. Gao, Y. Yu, Q. Ji. Information Theoretically Optimal Sample Complexity of Learning Dynamical Directed Acyclic Graphs. Arxiv preprint arXiv:2312.12844, 2023.
  [pdf] [Google scholar]

• M. S. Veedu, D. Deka, M. V. Salapaka . Information Theoretically Optimal Sample Complexity of Learning Dynamical Directed Acyclic Graphs. Arxiv preprint arXiv:2308.16859, 2023.
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• H. Kamkari, V. Zehtab, V. Balazadeh, R. G. Krishnan. OCDaf: Ordered Causal Discovery with Autoregressive Flows. Arxiv preprint arXiv:2308.07480, 2023.
  [pdf] [Google scholar]

• D. Fujiwara, K. Koyama, K. Kiritoshi, T. Okawachi, T. Izumitani, and S. Shimizu. Causal Discovery for Non-stationary Non-linear Time Series Data Using Just-In-Time Modeling. In Proc. 2nd Conference on Causal Learning and Reasoning, PMLR 213: 880-894, 2023.
  [pdf] [Google scholar]

• B. Duong, T. Nguyen. Heteroscedastic Causal Structure Learning. arXiv:2307.07973, 2023.
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• D. Kaltenpoth, J. Vreeken. Nonlinear Causal Discovery with Latent Confounders. In Proc. 40th International Conference on Machine Learning, PMLR 202:15639-15654, 2023.
  [pdf] [Google scholar]

• J. Bodik, V. Chavez-Demouli. Structural restrictions in local causal discovery: identifying direct causes of a target variable. arXiv:2307.1604, 2023.
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• J. Bodik, V. Chavez-Demoulin. Identifiability of causal graphs under nonadditive conditionally parametric causal models. Arxiv preprint arXiv:2303.15376, 2023.  
  [pdf] [Google scholar]

• R. A. Watson, H. Cai, X. An, S. McLean, R. Song. On Heterogeneous Treatment Effects in Heterogeneous Causal Graphs. Arxiv preprint arXiv:2301.12383, 2023.  
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• W. Gong, J. Jennings, C. Zhang, N. Pawlowski. Rhino: Deep Causal Temporal Relationship Learning With History-dependent Noise. Arxiv preprint arXiv:2210.14706, 2022. 
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• P. Sanchez, X. Liu, A. Q. O’Neil, S. A. Tsaftaris. DIFFUSION MODELS FOR CAUSAL DISCOVERY VIA TOPOLOGICAL ORDERING. Arxiv preprint arXiv:2205.13085 , 2022. 
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• G. Kikuchi. Differentiable Causal Discovery Under Heteroscedastic Noise. Proc. 29th Neural Information Processing (ICONIP2022), pp. 284-295, 2023.    
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• E. V. Strobl, T. A. Lasko. Identifying Patient-Specific Root Causes with the Heteroscedastic Noise Model. Arxiv preprint arXiv:2205.13085 , 2022. 
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• 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. 
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• 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.  
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• 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. 
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• I. Ng, Z. Fang, S. Zhu, Z. Chen. Masked Gradient-Based Causal Structure Learning. Arxiv preprint  arXiv:1910.08527, 2019.  
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• 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.  
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• 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.  
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• M. Rojas-Carulla, M. Baroni, and D. Lopez-Paz. Causal discovery using proxy variables. Arxiv preprint arXiv:1702.07306, 2017.
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• J. Peters. On the intersection property of conditional independence and its application to causal discovery. Journal of Causal Inference, 3(1): 97--108, 2014.
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• 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.
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• 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.
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• 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. Duong, H. Le, T. Nguyen. ALIAS: DAG Learning with Efficient Unconstrained Policies. arXiv preprint arXiv:2408.13448, 2024.
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• N. Hoang, B. Duong, T. Nguyen. Scalable Variational Causal Discovery Unconstrained by Acyclicity. arXiv preprint arXiv:2407.04992, 2024.
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• N. Hoang, B. Duong, T. Nguyen. Enabling Causal Discovery in Post-Nonlinear Models with Normalizing Flows. arXiv preprint arXiv:2407.04980, 2024.
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• S. Hiremath, J. R. M. A. Maasch, M. Gao, P. Ghosal, K. Gan. Hybrid Global Causal Discovery with Local Search. arXiv:2405.14496, 2024.
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• Z. Wang, X. Gao, X. Liu, X. Ru, Q. Zhang. Incorporating structural constraints into continuous optimization for causal discovery. Neurocomputing, xx(xx): 127902, 2024. 
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• N. Yin, T. Gao, Y. Yu, Q. Ji. Effective Causal Discovery under Identifiable Heteroscedastic Noise Model. In Proc. AAAI Conference on Artificial Intelligence, 38(15), 16486-16494, 2024. 
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• E. V. Bonilla, P. Elinas, H. Zhao, M. Filippone, V. Kitsios, T. O'Kane. Variational DAG Estimation via State Augmentation With Stochastic Permutations. arXiv:2402.02644, 2024. 
  [pdf] [Google scholar]

• M. Kertel, N. Klein. Boosting Causal Additive Models. arXiv:2401.06523, 2024. 
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• D. Waxman, K. Butler, P. M. Djuric. Dagma-DCE: Interpretable, Non-Parametric Differentiable Causal Discovery. IEEE Open Journal of Signal Processing, xx(xx): xx-xx, 2024.
  [pdf] [Google scholar]

• Q. D. Tran, B. Duong, P. Nguyen, T. Nguyen. Robust Estimation of Causal Heteroscedastic Noise Models. arXiv:2312.10102, 2023. 
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• B. Zhao, S. Wang, L. Chi, H. Yuan, Y. Yuan, Q. Li, J. Geng, S.-L. Zhang. Coresets for fast causal discovery with the additive noise model. Pattern Recognition, 148: 110149, 2024.
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• F. Montagna, N. Noceti, L. Rosasco, F. Locatello. Shortcuts for causal discovery of nonlinear models by score matching. arXiv:2310.14246, 2023. 
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• Z. Zhu, F. Locatello, V. Cevher. Sample Complexity Bounds for Score-Matching: Causal Discovery and Generative Modeling. arXiv:2310.18123, 2024. 
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• D. You, H. Wu, J. Liu, H. Yan, C. Ma, Z. Chen, X. Wu. Continuous causal structure learning from incremental instances and feature spaces. Information Fusion, 101: 101975, 2024. 
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• F. Montagna, N. Noceti, L. Rosasco, K. Zhang, F. Locatello. Causal Discovery with Score Matching on Additive Models with Arbitrary Noise. Proceedings of the Second Conference on Causal Learning and Reasoning (CLeaR2023), PMLR 213:752-771, 2023.
  [pdf] [Google scholar]

• F. Montagna, N. Noceti, L. Rosasco, K. Zhang, F. Locatello. Scalable Causal Discovery with Score Matching. Proceedings of the Second Conference on Causal Learning and Reasoning (CLeaR), PMLR 213:752-771, 2023.
  [pdf] [Google scholar]

• H Chen, M Kaptein. A novel framework extending cause-effect inference methods to multivariate causal discovery. arXiv:2305.16904, 2023. 
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• S. Akbari, L. Ganassali, N. Kiyavash. Learning Causal Graphs via Monotone Triangular Transport Maps. arXiv:2305.18210, 2023. 
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• F. Liu, W. Ma, A. Zhang, X. Wang, Y. Duan, T. S. Chua. Discovering Dynamic Causal Space for DAG Structure Learning. Arxiv preprint arXiv:2306.02822, 2023. 
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• A. G. Reisach, M. Tami, C. Seiler, A. Chambaz, S. Weichwald. Simple Sorting Criteria Help Find the Causal Order in Additive Noise Models. Arxiv preprint arXiv:2303.182115, 2023. 
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• Z. Chen, K. Liu, W. Jiao. A Genetic Algorithm for Causal Discovery Based on Structural Causal Model. In Proc. CAAI International Conference on Artificial Intelligence, pages 39-54, 2022.
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• M. Guardiani, P. Frank, A. Kostić, T. Enßlin. Towards Moment-Constrained Causal Modeling. Physical Science Forum 5(1), 7, 2022. 
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• W. Li, Y. Li, S. Zhu, Y. Shao, J. Hao, Y. Pang. GFlowCausal: Generative Flow Networks for Causal Discovery. Arxiv preprint arXiv:2210.08185, 2022. 
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• Z. Zhang, I. Ng, D. Gong, Y. Liu, E. M. Abbasnejad, M. Gong, K. Zhang, J. Q. Shi. MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models. Arxiv preprint arXiv:2208.14571, 2022. 
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• K. Uemura, T. Takagi, T. Kambayashi, H. Yoshida, and S. Shimizu. A Multivariate Causal Discovery based on Post-Nonlinear Model. In Proc. First Conference on Causal Learning and Reasoning (CLeaR2022), PMLR 177, pages 826-839, 2022.
  [pdf] [Google scholar]
  Python code

• E. Gao, I. Ng, M. Gong, L. Shen, W. Huang, T. Liu, K. Zhang, H. Bondell. MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models. Arxiv preprint arXiv:2205.13869, 2022. 
  [pdf] [Google scholar]

• P. Rolland, V. Cevher, M. Kleindessner, C. Russel, B. Schölkopf, D. Janzing, F. Locatello. Score matching enables causal discovery of nonlinear additive noise models. Arxiv preprint arXiv:2203.04413, 2022. 
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• J. Yang, L. Jiang, K. Xie, Q. Chen, A. Wang. Causal Structure Learning Algorithm Based on Partial Rank Correlation under Additive Noise Model. Applied Artificial Intelligence, xx: xx-xx, 2022.
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• B. Kap, M. Aleksandrova, T. Engel. Causal Identification with Additive Noise Models: Quantifying the Effect of Noise. Arxiv preprint arXiv:2108.08871, 2021. 
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• M. E. Jakobsen, R. D. Shah, P. Bühlmann, J. Peters. Structure Learning for Directed Trees. Arxiv preprint arXiv:2108.08871, 2021. 
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• 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.
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• 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.
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• 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]

• S. Lachapelle, P. Brouillard, T. Deleu, S. Lacoste-Julien. Gradient-Based Neural DAG Learning. In Proc. International Conference on Learning Representations (ICLR2020), pp. xx--xx, 2020.
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• 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]
  Python code

• M. Kurthen, T. Enßlin. A Bayesian Model for Bivariate Causal Inference. Entropy, 22(46): xx−xx, 2020.
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• B. Wang, Q. Zhou. Causal network learning with non-invertible functional relationships. Arxiv preprint arXiv: 2004.09646, 2020.
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• 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.
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• M. R. Heydari, S. Salehkaleybar, K. Zhang. Adversarial Orthogonal Regression: Two non-Linear Regressions for Causal Inference. Arxiv preprint  arXiv:1909.04454, 2019.  
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• I. Ng, S. Zhu, Z. Chen, Z. Fang. A Graph Autoencoder Approach to Causal Structure Learning. Arxiv preprint  arXiv:1911.07420, 2019. 
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• 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.
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• 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.
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• O. Goudet, D. Kalainathan, P. Caillou, I. Guyon, D. Lopez-Paz, and M. Sebag. Causal generative neural networks. Arxiv preprint arXiv:1711.08936, 2017.
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• 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.
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• 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.
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• 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.
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• Y. Hong, Z. Liu, and G. Mai. An efficient algorithm for large-scale causal discovery. Soft Computing, xx(xx): xx−xx, 2016.
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• 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.
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• 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.
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• 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.
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• 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.
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• 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.
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• 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.
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• 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.
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• 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.
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• 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.
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• 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.
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• C. Nowzohour and P. Bühlmann. Score-based causal learning in additive noise models. Arxiv preprint arXiv:1311.6359, 2013.
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• R. Henao and O. Winther. Sparse linear identifiable multivariate modeling. Journal of Machine Learning Research, 12(Mar): 863--905, 2011.
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• 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.
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• 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.
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• 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.
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• 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.
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• 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.
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• 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.
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• Y. Tamada, S. Imoto and S. Miyano. Parallel algorithm for learning optimal Bayesian network structure. Journal of Machine Learning Research, 12: 2437–2459, 2011.
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• 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.
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• 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.
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Others

• F. Montagna, P. M. Faller, P. Bloebaum, E. Kirschbaum, F. Locatello. Score matching through the roof: linear, nonlinear, and latent variables causal discovery. arXiv:2407.18755, 2024.
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• T. N. Maeda and S. Shimizu. Use of prior knowledge to discover causal additive models with unobserved variables and its application to time series data. Behaviormetrika, xx(xx): xx-xx, 2024.
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• A. Rahmani, P. Frossard. CASTOR: CAUSAL TEMPORAL REGIME STRUCTURE LEARNING. arXiv:2311.01412, 2023.
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• Y. Ni and S. Chen. Casual Structural Modeling of Survey Questionnaires via a Bootstrapped Ordinal Bayesian Network Appr. PsyArXiv, 2 June 2023, 2023.
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• B. Zhao, S. Wang, L. Chi, C. Zhao, H. Yuan, Q, Li, X, Liu, J. Geng, , Y. Yuan. HANM: Hierarchical Additive Noise Model for Many-to-One Causality Discovery. IEEE Transactions on Knowledge and Data Engineering, 2023.
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• H. Morioka and A. Hyvarinen. Connectivity-Contrastive Learning: Combining Causal Discovery and Representation Learning for Multimodal Data. In Proc. 26th International Conference on Artificial Intelligence and Statistics, PMLR, 206:3399-3426, 2023.
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• H. Morioka and A. Hyvarinen. Connectivity-Contrastive Learning: Combining Causal Discovery and Representation Learning for Multimodal Data. In Proc. 26th International Conference on Artificial Intelligence and Statistics, PMLR, 206:3399-3426, 2023.
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• C. Li, X. Shen, W. Pan. Nonlinear Causal Discovery with Confounders. Journal of the American Statistical Association, xx(x): xx, 2023.
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• D. Wang, Z. Chen, J. Ni, L. Tong, Z. Wang, Y. Fu, H. Chen. Hierarchical Graph Neural Networks for Causal Discovery and Root Cause Localization. arXiv:2302.01987, 2023.
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• X. Sun, O. Schulte. Cause-Effect Inference in Location-Scale Noise Models: Maximum Likelihood vs. Independence Testing. Arxiv preprint arXiv:2301.12930, 2023.
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• F. Cordoni, N. Doremus, A. Moneta. Identification of Vector Autoregressive Models with Nonlinear Contemporaneous Structure.  LEM Papers Series 2023/07, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy. 
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• T. Wu, X. Wu, X. Wang, S. Liu, H. Chen. Nonlinear Causal Discovery in Time Series. In Proc. 31st ACM International Conference on Information & Knowledge Management (CIKM2022), pages 4575–4579, 2022.
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