Latent confounders and latent factors

Latent variables including latent confounders and latent factors

Models with latent confounding variables

  • 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

  • NEW 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 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. Arxiv preprint arXiv:2009.09176, 2020.
    [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]