Latent confounders and latent factors

Latent variables including latent confounders and latent factors

Models with latent confounding variables

  • F. Zhou, K. He, Y. Ni. Causal Discovery with Heterogeneous Observational Data. arXiv:2201.12392, 2022.
    [pdf] [Google schlar]

  • J. Adams, N. Hansen, K. Zhang . Identification of Partially Observed Linear Causal Models: Graphical Conditions for the Non-Gaussian and Heterogeneous Cases. In Advances in Neural Information Processing Systems 35 (NeurIPS2021), pp. xx-xx, 2021.
    [pdf] [Google scholar]

  • W. Chen, R. Cai, K. Zhang, Z. Hao. Causal Discovery in Linear Non-Gaussian Acyclic Model With Multiple Latent Confounders. IEEE Transactions on Neural Networks and Learning Systems, xx(x): xx-xx, 2021.[pdf] [Google scholar]

  • Y. Liu, E. Robeva, and H. Wang. Learning Linear Non-Gaussian Graphical Models with Multidirected Edges. arXiv:2010.05306 , 2020.
    [pdf] [Google schlar]

  • S. Salehkaleybar, A. Ghassami, N. Kiyavash, K. Zhang. Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables. Journal of Machine Learning Research, 21:1-24, 2020.
    [pdf] [Google scholar]

  • Y. S. Wang, M. Drton. Causal Discovery with Unobserved Confounding and non-Gaussian Data. arXiv:2007.11131, 2020.
    [pdf] [Google schlar]

  • P. Geiger, K. Zhang, M. Gong, D. Janzing, and B. Schölkopf. Causal inference by identification of vector autoregressive processes with hidden components. In Proc. 32nd International Conference on Machine Learning (ICML2015), pp. xx-xx, Lille, France, 2015.
    [pdf] [Google scholar]

  • S. Shimizu and K. Bollen. Bayesian estimation of causal direction in acyclic structural equation models with individual-specific confounder variables and non-Gaussian distributions. Journal of Machine Learning Research, 15: 2629-2652, 2014.
    [pdf] [Python code] [Google scholar]

  • W. Gao and H. Yang. Identifying structural VAR model with latent variables using overcomplete ICA. Far East Journal of Theoretical Statistics, 40(1): 31-44, 2012.
    [pdf] [Google scholar]

  • Z. Chen and L. Chan. Causality in linear nongaussian acyclic models in the presence of latent Gaussian confounders. Neural Computation, 25(6): 1605-1641, 2013.
    [pdf] [Google scholar]

  • Z. Chen and L. Chan. Causal discovery for linear non-Gaussian acyclic models in the presence of latent Gaussian confounders. In Proc. 10th International Conference on Latent Variable Analysis and Signal Separation (LVA/ICA2012), Tel Aviv, Israel, pp.17--24, 2012.
    [pdf] [Google scholar]

  • P. O. Hoyer, S. Shimizu, A. Kerminen and M. Palviainen. Estimation of causal effects using linear non-gaussian causal models with hidden variables. International Journal of Approximate Reasoning, 49(2): 362-378, 2008.
    [pdf] [Matlab code] [Google scholar]

  • P. O. Hoyer, S. Shimizu and A. Kerminen. Estimation of linear, non-gaussian causal models in the presence of confounding latent variables. In Proc. the third European Workshop on Probabilistic Graphical Models (PGM2006), pp. 155--162, Prague, Czech Republic, 2006.
    [pdf] [Google scholar]

Estimation of models with latent confounding

  • W. Chen, K. Zhang, R. Cai, B. Huang, J. Ramsey, Z. Hao, C. Glymour. FRITL: A Hybrid Method for Causal Discovery in the Presence of Latent Confounders. Arxiv preprint arXiv:2103.14238, 2021.
    [pdf] [Google schlar]

  • E. Robeva, J.-B. Seby. Multi-trek separation in Linear Structural Equation Models. Arxiv preprint arXiv:2001.10426, 2020.
    [pdf] [Google schlar]

  • T. N. Maeda, S. Shimizu. RCD: Repetitive causal discovery of linear non-Gaussian acyclic models with latent confounders. In Proc. 23rd International Conference on Artificial Intelligence and Statistics (AISTATS2020), Palermo, Sicily, Italy. PMLR: Volume xx.
    [pdf] [Google scholar]

  • C. Ding, M. Gong, K. Zhang, D. Tao. Likelihood-Free Overcomplete ICA and Applications in Causal Discovery. In Advances in Neural Information Processing Systems 33 (NIPS2019), pp. xx-xx, 2019.
    [pdf] [Google scholar]

  • S. Shimizu. A non-Gaussian approach for causal discovery in the presence of hidden common causes. In Proc. Second Workshop on Advanced Methodologies for Bayesian Networks (AMBN2015), pp. 222--233, Yokohama, Japan, 2015.
    [pdf] [Google scholar]

  • T. Tashiro, S. Shimizu, A. Hyvärinen and T. Washio. ParceLiNGAM: A causal ordering method robust against latent confounders. Neural Computation, 26(1): 57--83, 2014.
    [pdf] [code] [Google scholar]

  • T. Tashiro, S. Shimizu, A. Hyvärinen and T. Washio. Estimation of causal orders in a linear non-Gaussian acyclic model: a method robust against latent confounders. In Proc. 22nd International Conference on Artificial Neural Networks (ICANN2012), pp. 491--498, Lausanne, Switzerland, 2012.
    [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]

  • 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.
    [pdf] [code] [Google scholar]

Others

  • NEW Z. Chen, F. Xie, J. Qiao, Z. Hao, K. Zhang, R. Cai. Identification of Linear Latent Variable Model with Arbitrary Distribution. In Proc. 36th AAAI Conference on Artificial Intelligence (AAAI2022), pp. xx-xx, 2022.
    [pdf] [Google scholar]

  • X. Li, M. P. Martens, W. Wiedermann. Conditional Direction of Dependence Modeling: Application and Implementation in SPSS. Social Science Computer Review, xx(xx): xx-xx, 2022.
    [pdf] [Google scholar]

  • V. P. Nia, X. Li, M. Asgharian, S. Hu, Y. Geng, Z. Chen. A causal direction test for heterogeneous populations. Machine Learning with Applications, xx(xx): xx-xx, 2021.
    [pdf] [Google scholar]

  • W. Chen, Y. Wu, R. Cai, Y. Chen, Z. Hao. CCSL: A Causal Structure Learning Method from Multiple Unknown Environments. arXiv preprint arXiv:2111.09666, 2021.
    [pdf] [Google scholar]

  • W. Yao, Y. Sun, A. Ho, C. Sun, K. Zhang. Learning Temporally Causal Latent Processes from General Temporal Data. ArXiv preprint arXiv:2110.05428, 2021.
    [pdf] [Google scholar]

  • F. Xie, R. Cai, B. Huang, C. Glymour, Z. Hao, K. Zhang. Generalized Independent Noise Condition for Estimating Linear Non-Gaussian Latent Variable Graphs. In Advances in Neural Information Processing Systems 34 (NeurIPS2020), pp. xx-xx, 2020.
    [pdf] [Google scholar]

  • Y. Zeng, S. Shimizu, R. Cai, F. Xie, M. Yamamoto, Z. Hao. Causal Discovery with Multi-Domain LiNGAM for Latent Factors. In Proc. 30th International Joint Conference on Artificial Intelligence (IJCAI-21), 2021.
    [pdf] [Google scholar]

  • M.P. van Wie, X. Li, W. Wiedermann. Identification of confounded subgroups using linear model-based recursive partitioning. Psychological Test and Assessment Modeling, 61(4): 365-387, 2019.[
    pdf] [Google scholar]

  • R. Cai, F. Xie, C. Glymour, Z. Hao, K. Zhang. Triad Constraints for Learning Causal Structure of Latent Variables. In Advances in Neural Information Processing Systems 33 (NeurIPS2019), pp. xx-xx, 2019.
    [pdf] [Google scholar]

  • F. Xie, R. Cai, Y. Zeng, J. Gao, Z. Hao. An Efficient Entropy-Based Causal Discovery Method for Linear Structural Equation Models With IID Noise Variables. IEEE Transactions on Neural Networks and Learning Systems, xx: xx-xx, 2019.
    [pdf] [Google scholar]

  • K. Zhang, M. Gong, J. Ramsey, K. Batmanghelich, P. Spirtes, and C. Glymour. Causal discovery in the presence of measurement error: Identifiability conditions. In Proc. 34th Conf. on Uncertainty in Artificial Intelligence (UAI2018), pp. xx-xx, Montreal, Canada, 2018.
    [pdf] [Google scholar]

  • R. Pio Monti, A. Hyvärinen. A unified probabilistic model for learning latent factors and their connectivities from high-dimensional data. In Proc. 34th Conf. on Uncertainty in Artificial Intelligence (UAI2018), pp. xx-xx, Montreal, Canada, 2018.
    [pdf] [Google scholar]

  • N. Tanaka, S. Shimizu, and T. Washio. A Bayesian estimation approach to analyze non-Gaussian data-generating processes with latent classes. Arxiv preprint arXiv:1408.0337, 2014.
    [pdf] [Google scholar]

  • A. von Eye and W. Wiedermann. On direction of dependence in latent variable contexts. Educational and Psychological Measurement, 74(1): 5-30, 2014.
    [pdf] [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.
    [pdf] [Google scholar]

  • Y. Kawahara, K. Bollen, S. Shimizu and T. Washio. GroupLiNGAM: Linear non-Gaussian acyclic models for sets of variables. Arxiv preprint arXiv:1006.5041, 2010.
    [pdf] [Google scholar]

  • S. Shimizu, P. O. Hoyer and A. Hyvärinen. Estimation of linear non-Gaussian acyclic models for latent factors.Neurocomputing, 72: 2024-2027, 2009.
    [pdf] [Google scholar]

  • S. Shimizu and A. Hyvärinen. Discovery of linear non-gaussian acyclic models in the presence of latent classes. In Proc. 14th Int. Conf. on Neural Information Processing (ICONIP2007), pp. 752-761, Kitakyushu, Japan, 2008.
    [pdf] [Google scholar]