Kernel-based Approach for Learning Causal Graphs from Mixed Data

Teny Handhayani*, James Cussens

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

4 Citations (Scopus)


A causal graph can be generated from a dataset using a particular causal algorithm, for instance, the PC algorithm or Fast Causal Inference (FCI). This paper provides two contributions in learning causal graphs: an easy way to handle mixed data so that it can be used to learn causal graphs using the PC algorithm/FCI and a method to evaluate the learned graph structure when the true graph is unknown. This research proposes using kernel functions and Kernel Alignment to handle mixed data. The two main steps of this approach are computing a kernel matrix for each variable and calculating a pseudo-correlation matrix using Kernel Alignment. The Kernel Alignment matrix is used as a substitute for the correlation matrix that is the main component used in computing a partial correlation 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 more data types when there is 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. We also introduce the Modal Value of Edges Existence (MVEE) method, a new method to evaluate the structure of learned graphs represented by Partial Ancestral Graph (PAG) when the true graph is unknown. MVEE produces an agreement graph as a proxy to the true graph to evaluate the structure of the learned graph. MVEE is successfully used to choose the best-learned graph when the true graph is unknown.
Original languageEnglish
Title of host publicationProceedings of Machine Learning Research
Subtitle of host publicationProceedings of the 10th International Conference on Probabilistic Graphical Models
EditorsManfred Jaeger, Thomas D. Nielsen
Publication statusPublished - 25 Sept 2020
Event10th International Conference on Probabilistic Graphical Models - Hotel Comwell Rebild Bakker, Skørping, Denmark
Duration: 23 Sept 202025 Sept 2020

Publication series

NameProceedings of Machine Learning Research
ISSN (Electronic)2640-3498


Conference10th International Conference on Probabilistic Graphical Models
Abbreviated titlePGM 2020
Internet address

Bibliographical note

Funding Information:
This work was supported through a scholarship managed by Lembaga Pengelola Dana Pendidikan Indonesia (Indonesia Endowment Fund for Education)


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