Estimation of Structural Causal Model via Sparsely Mixing Independent Component Analysis
arXiv:2009.03077 · doi:10.1016/j.neucom.2021.06.083
Abstract
We consider the problem of inferring the causal structure from observational data, especially when the structure is sparse. This type of problem is usually formulated as an inference of a directed acyclic graph (DAG) model. The linear non-Gaussian acyclic model (LiNGAM) is one of the most successful DAG models, and various estimation methods have been developed. However, existing methods are not efficient for some reasons: (i) the sparse structure is not always incorporated in causal order estimation, and (ii) the whole information of the data is not used in parameter estimation. To address {these issues}, we propose a new estimation method for a linear DAG model with non-Gaussian noises. The proposed method is based on the log-likelihood of independent component analysis (ICA) with two penalty terms related to the sparsity and the consistency condition. The proposed method enables us to estimate the causal order and the parameters simultaneously. For stable and efficient optimization, we propose some devices, such as a modified natural gradient. Numerical experiments show that the proposed method outperforms existing methods, including LiNGAM and NOTEARS.
arXiv comment9 pages, 6 figures. Neurocomputing (2021)
References in corpus (7)
- Emergence of scaling in random networks
- The solution path of the generalized lasso
- On the Identifiability of the Post-Nonlinear Causal Model
- Identifiability of Gaussian structural equation models with equal error variances
- DAGs with NO TEARS: Continuous Optimization for Structure Learning
- Causal Discovery with Continuous Additive Noise Models
- On the Role of Sparsity and DAG Constraints for Learning Linear DAGs