学习并评分带有未知加性干预的高斯潜变量因果模型
Learning and scoring Gaussian latent variable causal models with unknown additive interventions
AI总结：
针对未知性质扰动下含潜变量的因果结构学习难题，提出利用系统不变性的最大似然估计器输出干预等价类，给出图形表征并通过多类数据验证，配套Python包utlvce。
摘要：
仅使用观测数据时，因果结构学习是一项具有挑战性的任务。当能够获取从底层系统的扰动中收集的数据时，即使这些扰动的性质未知，该任务也会变得更容易。现有方法要么不允许潜变量存在，要么假设这些潜变量不会受到扰动。然而，如果扰动的性质未知，这些假设很难成立。我们提出了相关结果，可在存在加性但未知的干预的场景下对因果结构进行评分。具体而言，我们在结构方程模型中提出了一种最大似然估计器，该估计器利用系统层面的不变性，从扰动数据中输出因果结构的等价类。此外，在对总体模型的某些结构假设下，我们给出了干预等价类中所有有向无环图（DAG）的简单图形表征。我们在合成数据以及涉及加利福尼亚州水库和蛋白质表达的真实数据上验证了所提框架的实用性。其软件实现以Python包utlvce的形式提供。
英文摘要：
With observational data alone, causal structure learning is a challenging problem. The task becomes easier when having access to data collected from perturbations of the underlying system, even when the nature of these is unknown. Existing methods either do not allow for the presence of latent variables or assume that these remain unperturbed. However, these assumptions are hard to justify if the nature of the perturbations is unknown. We provide results that enable scoring causal structures in the setting with additive, but unknown interventions. Specifically, we propose a maximum-likelihood estimator in a structural equation model that exploits system-wide invariances to output an equivalence class of causal structures from perturbation data. Furthermore, under certain structural assumptions on the population model, we provide a simple graphical characterization of all the DAGs in the interventional equivalence class. We illustrate the utility of our framework on synthetic data as well as real data involving California reservoirs and protein expressions. The software implementation is available as the Python package \emph{utlvce}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
