Causal discovery based on heterogeneous non-Euclidean data
Causal relationships play an irreplaceable role in revealing the mechanisms of phe-nomena and guiding intervention actions.However,due to limitations in existing frameworks regarding model representations and learning algorithms,only a few studies have explored causal discovery on non-Euclidean data.In this paper,we address the issue by proposing a causal mapping process based on coordinate representations for heterogeneous non-Euclidean data.We propose a data generation mechanism between the parent nodes and the child nodes and create a causal mechanism based on multi-dimensional tensor regression.Furthermore,within the afore-mentioned theoretical framework,we propose a two-stage causal discovery approach based on regularized generalized canonical correlation analysis.Using the discrete representation in the shared projection direction,causal relationships between heterogeneous non-Euclidean variables can be discovered more accurately.Finally,empirical research is conducted on real-world indus-trial sensor data,which demonstrates the effectiveness of the proposed method for discovering causal relationships in heterogeneous non-Euclidean data.
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