  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.
