Package: lingam
LiNGAM - Discovery of non-gaussian linear causal modelsLiNGAM is a new method for estimating structural equation models or linear Bayesian networks. It is based on using the non-Gaussianity of the data.The LiNGAM ProjectRequirementsPython3numpyscipyscikit-learngraphvizstatsmodelsnetworkxpandasitertoolssemopyInstallationTo install lingam package, usepipas follows:pipinstalllingamUsageimportnumpyasnpimportpandasaspdimportlingam# To run causal discovery, we create a DirectLiNGAM object and call the fit method.model=lingam.DirectLiNGAM()model.fit(X)# Using the causal_order_ properties,# we can see the causal ordering as a result of the causal discovery.print(model.causal_order_)# Also, using the adjacency_matrix_ properties,# we can see the adjacency matrix as a result of the causal discovery.print(model.adjacency_matrix_)DocumentationTutorial and API referenceTutorial slidesExamplesWe provide several examples of running the LiNGAM algorithm in Jupyter Notebook.lingam/examplesLicenseThis project is licensed under the terms of theMIT license.ContributionFor guidelines how to contribute to lingam package, take a look atCONTRIBUTING.md.ReferencesPackageIf you find our package useful, please cite the following paper:T. Ikeuchi, M. Ide, Y. Zeng, T. N. Maeda, and S. Shimizu.Python package for causal discovery based on LiNGAM.Journal of Machine Learning Research, 24(14): 1−8, 2023.[PDF]Basic DAG modelShould you use this package for performingICA-based LiNGAM algorithm, we kindly request you to cite the following paper:S. Shimizu, P. O. Hoyer, A. Hyvärinen, and A. Kerminen.A linear non-gaussian acyclic model for causal discovery.Journal of Machine Learning Research, 7: 2003--2030, 2006.[PDF]Should you use this package for performingDirectLiNGAM algorithm, we kindly request you to cite the following two papers:S. Shimizu, T. Inazumi, Y. Sogawa, A. Hyvärinen, Y. Kawahara, T. Washio, P. O. Hoyer and K. Bollen.DirectLiNGAM: A direct method for learning a linear non-Gaussian structural equation model.Journal of Machine Learning Research, 12(Apr): 1225--1248, 2011.[PDF]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]Should you use this package for performingRESIT algorithm, we kindly request you to cite the following paper:J. Peters, J. M. Mooij, D. Janzing, and B. Schölkopf.Causal Discovery with Continuous Additive Noise Models.Journal of Machine Learning Research, 15(58): 2009--2053, 2014.[PDF]Time seriesShould you use this package for performingVAR-LiNGAM, we kindly request you to cite the following paper:A. Hyvärinen, K. Zhang, S. Shimizu, and P. O. Hoyer.Estimation of a structural vector autoregression model using non-Gaussianity.Journal of Machine Learning Research, 11: 1709-1731, 2010.[PDF]Should you use this package for performingVARMA-LiNGAM, we kindly request you to cite the following paper:Y. Kawahara, S. Shimizu and T. Washio.Analyzing relationships among ARMA processes based on non-Gaussianity of external influences.Neurocomputing, 74(12-13): 2212-2221, 2011.[PDF]Multiple datasetsShould you use this package for performingDirectLiNGAM for multiple groups, we kindly request you to cite the following paper:S. Shimizu.Joint estimation of linear non-Gaussian acyclic models.Neurocomputing, 81: 104-107, 2012.[PDF]Should you use this package for performingLiNGAM for longitudinal data, we kindly request you to cite the following paper: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.[PDF]Latent confounders and latent factorsShould you use this package for performingBottomUpParceLiNGAMwith Algorithm 1 of the paper below except Step 2 for estimating causal orders, we kindly request you to cite the following paper:T. Tashiro, S. Shimizu, A. Hyvärinen, T. Washio.ParceLiNGAM: a causal ordering method robust against latent confounders. Neural computation, 26(1): 57-83, 2014.[PDF]Should you use this package for performingRCD algorithm, we kindly request you to cite the following paper:T. N. Maeda and 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  108:735-745, 2020.[PDF]Should you use this package for performingLiNA algorithm, we kindly request you to cite the following paper:Y. Zeng, S. Shimizu, R. Cai, F. Xie, M. Yamamoto and Z. Hao.Causal Discovery with Multi-Domain LiNGAM for Latent Factors. In Proc. of the Thirtieth International Joint Conference on Artificial Intelligence (IJCAI-21), 2021: 2097--2103. [PDF]Should you use this package for performingCAM-UV algorithm, we kindly request you to cite the following paper:T. N. Maeda and S. Shimizu.Causal additive models with unobserved variables.In Proc. 37th Conference on Uncertainty in Artificial Intelligence (UAI). PMLR 161:97-106, 2021.[PDF]Causality and predictionShould you use this package for performingestimation of intervention effects on prediction, we kindly request you to cite the following paper: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. 1--6, Tokyo, Japan, 2017.[PDF]Mixed dataShould you use this package for performingLiM algorithm, we kindly request you to cite the following paper:Y. Zeng, S. Shimizu, H. Matsui, F. Sun.Causal discovery for linear mixed data. In Proc. First Conference on Causal Learning and Reasoning (CLeaR2022). PMLR 177, pp. 994-1009, 2022.[PDF]