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

Kernel-based Approach to Handle Mixed Data for Inferring Causal Graphs