DirectLiNGAM: A Direct Method for Learning a Linear Non-Gaussian Structural Equation Model. Structural equation models and Bayesian networks have been widely used to analyze causal relations between continuous variables. In such frameworks, linear acyclic models are typically used to model the data-generating process of variables. Recently, it was shown that use of non-Gaussianity identifies the full structure of a linear acyclic model, that is, a causal ordering of variables and their connection strengths, without using any prior knowledge on the network structure, which is not the case with conventional methods. However, existing estimation methods are based on iterative search algorithms and may not converge to a correct solution in a finite number of steps. In this paper, we propose a new direct method to estimate a causal ordering and connection strengths based on non-Gaussianity. In contrast to the previous methods, our algorithm requires no algorithmic parameters and is guaranteed to converge to the right solution within a small fixed number of steps if the data strictly follows the model, that is, if all the model assumptions are met and the sample size is infinite.

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  1. Gnecco, Nicola; Meinshausen, Nicolai; Peters, Jonas; Engelke, Sebastian: Causal discovery in heavy-tailed models (2021)
  2. Park, Gunwoong; Kim, Yesool: Learning high-dimensional Gaussian linear structural equation models with heterogeneous error variances (2021)
  3. Park, Gunwoong; Moon, Sang Jun; Park, Sion; Jeon, Jong-June: Learning a high-dimensional linear structural equation model via (\ell_1)-regularized regression (2021)
  4. Robeva, Elina; Seby, Jean-Baptiste: Multi-trek separation in linear structural equation models (2021)
  5. Wang, Bingling; Zhou, Qing: Causal network learning with non-invertible functional relationships (2021)
  6. Park, Gunwoong: Identifiability of additive noise models using conditional variances (2020)
  7. Salehkaleybar, Saber; Ghassami, Amiremad; Kiyavash, Negar; Zhang, Kun: Learning linear non-Gaussian causal models in the presence of latent variables (2020)
  8. Zeng, Yan; Hao, Zhifeng; Cai, Ruichu; Xie, Feng; Ou, Liang; Huang, Ruihui: A causal discovery algorithm based on the prior selection of leaf nodes (2020)
  9. Park, Gunwoong; Park, Sion: High-dimensional Poisson structural equation model learning via (\ell_1)-regularized regression (2019)
  10. Hu, Shoubo; Chen, Zhitang; Chan, Laiwan: A kernel embedding-based approach for nonstationary causal model inference (2018)
  11. Liu, Furui; Chan, Laiwan: Confounder detection in high-dimensional linear models using first moments of spectral measures (2018)
  12. Wiedermann, Wolfgang; Merkle, Edgar C.; von Eye, Alexander: Direction of dependence in measurement error models (2018)
  13. Parida, Pramod Kumar; Marwala, Tshilidzi; Chakraverty, Snehashish: Altered-LiNGAM (ALiNGAM) for solving nonlinear causal models when data is nonlinear and noisy (2017)
  14. Parida, Pramod Kumar; Marwala, Tshilidzi; Chakraverty, Snehashish: An overview of recent advancements in causal studies (2017)
  15. Liu, Furui; Chan, Laiwan: Causal inference on discrete data via estimating distance correlations (2016)
  16. Mooij, Joris M.; Peters, Jonas; Janzing, Dominik; Zscheischler, Jakob; Schölkopf, Bernhard: Distinguishing cause from effect using observational data: methods and benchmarks (2016)
  17. Chen, Zhitang; Zhang, Kun; Chan, Laiwan; Schölkopf, Bernhard: Causal discovery via reproducing kernel Hilbert space embeddings (2014)
  18. Sokol, Alexander; Maathuis, Marloes H.; Falkeborg, Benjamin: Quantifying identifiability in independent component analysis (2014)
  19. Tashiro, Tatsuya; Shimizu, Shohei; Hyvärinen, Aapo; Washio, Takashi: ParceLiNGAM: a causal ordering method robust against latent confounders (2014)
  20. Chen, Zhitang; Chan, Laiwan: Causality in linear nongaussian acyclic models in the presence of latent Gaussian confounders (2013)

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