  Causal learning is a beneficial approach to analyze the cause and effect
relationships among variables in a dataset. A causal graph can be generated
from a dataset using a particular causal algorithm, for instance, the PC
algorithm or Fast Causal Inference (FCI). Generating a causal graph from a
dataset that contains different data types (mixed data) is not trivial. This
research offers an easy way to handle the mixed data so that it can be used to
learn causal graphs using the existing application of the PC algorithm and FCI.
This research proposes using kernel functions and Kernel Alignment to handle
mixed data. Two main steps of this approach are computing a kernel matrix for
each variable and calculating a pseudo-correlation matrix using Kernel
Alignment. Kernel Alignment is used as a substitute for the correlation matrix
for the conditional independence test for Gaussian data in the PC Algorithm and
FCI. The advantage of this idea is that is possible to handle any data type by
using a suitable kernel function to compute a kernel matrix for an observed
variable. The proposed method is successfully applied to learn a causal graph
from mixed data containing categorical, binary, ordinal, and continuous
variables.
