In recent years, causal modelling has been used widely to improve generalization and to provide interpretability in machine learning models. To determine cause-effect relationships in the absence of a randomized trial, we can model causal systems with counterfactuals and interventions given enough domain knowledge. However, there are several cases where domain knowledge is almost absent and the only recourse is using a statistical method to estimate causal relationships. While there have been several works done in estimating causal relationships in unstructured data, we are yet to find a well-defined framework for estimating causal relationships in Knowledge Graphs (KG). It is commonly used to provide a semantic framework for data with complex inter-domain relationships. In this work, we define a hybrid approach that allows us to discover cause-effect relationships in KG. The proposed approach is based around the finding of the instantaneous causal structure of a non-experimental matrix using a non-Gaussian model, i.e; finding the causal ordering of the variables in a non-Gaussian setting. The non-experimental matrix is a low-dimensional tensor projection obtained by decomposing the adjacency tensor of a KG. We use two different pre-existing algorithms, one for the causal discovery and the other for decomposing the KG and combining them to get the causal structure in a KG.
翻译:近些年来,广泛使用因果建模来改进通用,并提供机器学习模型的可解释性。为了在没有随机试验的情况下确定因果关系,我们可以在没有随机试验的情况下以反事实和干预措施来模拟因果系统,并有足够的领域知识。然而,有几例领域知识几乎不存在,唯一的办法是使用统计方法来估计因果关系。虽然在估计非结构化数据中的因果关系方面做了一些工作,但我们尚未找到一个明确界定的框架来估计知识图(KG)中的因果关系。通常用于为具有复杂多领域关系的数据提供一个语义框架。在这项工作中,我们界定了一种混合方法,使我们能够发现在KG中存在的因果关系。 提议的方法是基于利用非伽西模式的非因果矩阵的瞬间因果结构,即:在非加西语环境中找到因果联系的因果联系。非加西语系矩阵是一种低维度的高压图,通过分解使KG的因果变异的因果结构,我们用KG的因果结构来进行。