发表机构
University of Maryland Baltimore County（马里兰大学巴尔的摩县分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出一种样本依赖感知的盲源分离方法，通过将互信息率替代互信息代价，扩展线性非高斯非循环模型（LiNGAM）的因果可识别性，联合利用高阶统计量和样本依赖，在合成及fMRI数据上实现近乎完美的因果恢复并提升稳定性。
AI 中文摘要
因果发现旨在确定复杂系统中变量之间的方向性关系，然而仅凭观测数据通常无法识别因果方向。因此，完整的识别需要在线性性和非循环性之外附加假设。一种关键方法是线性非高斯非循环模型（LiNGAM），它将线性结构方程模型估计表述为盲源分离（BSS）问题。LiNGAM在独立采样下将扰动视为相互独立的随机变量，依赖高阶统计量（HOS）和非高斯性来实现可识别性。这限制了当多个扰动为高斯分布时的可识别性，并忽略了潜在的信息性时间依赖性。为了拓宽因果可识别性，我们将具有相互独立扰动过程的线性非循环因果发现公式化，并将已建立的BSS识别条件迁移到因果结构恢复中。在此公式下，将互信息代价替换为互信息率，可以在更广泛的扰动类别下实现识别，包括具有非比例协方差函数的高斯过程。合成实验揭示了仅依赖HOS或样本依赖的方法的局限性，而它们的联合利用在测试的扰动设置中提供了近乎完美的因果恢复。对真实功能磁共振成像（fMRI）数据的实验进一步表明，联合利用HOS和样本依赖提高了估计祖先方向性的自举稳定性。
英文摘要
Causal discovery seeks to determine directional relationships among variables in complex systems, yet observational data alone generally do not identify causal direction. Complete identification therefore requires assumptions beyond linearity and acyclicity. A key approach is the Linear Non-Gaussian Acyclic Model (LiNGAM), which formulates linear structural equation model estimation as a blind source separation (BSS) problem. LiNGAM treats disturbances as mutually independent random variables under independent sampling, relying on higher-order statistics (HOS) and non-Gaussianity for identifiability. This restricts identifiability when multiple disturbances are Gaussian and ignores potentially informative temporal dependence. To broaden causal identifiability, we formulate linear acyclic causal discovery with mutually independent disturbance processes and transfer established BSS identification conditions to causal structure recovery. Under this formulation, replacing the mutual information cost with mutual information rate enables identification under a broader class of disturbances, including Gaussian processes with nonproportional covariance functions. Synthetic experiments reveal the limitations of methods relying solely on HOS or sample dependence, while their joint exploitation provides near-perfect causal recovery across the tested disturbance settings. Experiments on real functional magnetic resonance imaging (fMRI) data further demonstrate that jointly exploiting HOS and sample dependence improves the bootstrap stability of estimated ancestral directionality.
Comments5 pages, 2 figures. Submitted to the 2027 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
