论文标题
通过稀疏混合独立组件分析来估计结构性因果模型
Estimation of Structural Causal Model via Sparsely Mixing Independent Component Analysis
论文作者
论文摘要
我们考虑从观察数据中推断因果结构的问题，尤其是当结构稀疏时。这种类型的问题通常被表达为定向无环图（DAG）模型的推断。线性非高斯无环模型（lingam）是最成功的DAG模型之一，并且已经开发出各种估计方法。但是，由于某些原因，现有方法并不有效：（i）稀疏结构并不总是在因果顺序估计中纳入，并且（ii）数据的全部信息未在参数估计中使用。为了解决{这些问题}，我们为带有非高斯噪音的线性DAG模型提出了一种新的估计方法。该方法基于独立组件分析（ICA）的对数可能性，具有与稀疏性和一致性条件相关的两个惩罚项。提出的方法使我们能够同时估计因果秩序和参数。为了进行稳定，有效的优化，我们提出了一些设备，例如修改的自然梯度。数值实验表明，所提出的方法的表现优于现有方法，包括林加姆和notears。
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.
