Causal Discovery Based on Hybrid Structural Equation Model
Xing Zhou, Yaping Wan · 2022
Causal relation is the cornerstone of human understanding and exploration of the world. Inferring causal relations between things has been of interest to researchers. Most traditional methods are designed purely for discrete or continuous data, yet mixed data are widely available. This paper proposes a causal discovery method based on a hybrid structural equation model. The main idea is to formulate a nonlinear causal mechanism for mixed data through a hybrid structural equation model, while incorporating the ideas of structural equation and probabilistic noise in likelihood maximization, which realizes efficient causal inference on mixed data. Experimental results on synthetic and real-world datasets show that the method improves the accuracy of causal inference for mixed data and it’s robust to anomalous data.