Temporal latent variable structural causal model for causal discovery under external interferences

Inferring causal relationships from observed data is an important task, yet it becomes challenging when the data is subject to various external interferences. Most of these interferences are the additional effects of external factors on observed variables. Since these external factors are often unkn...

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Vydáno v:Neurocomputing (Amsterdam) Ročník 640; s. 130281
Hlavní autoři: Cai, Ruichu, Huang, Xiaokai, Chen, Wei, Li, Zijian, Hao, Zhifeng
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 01.08.2025
Témata:
Causal discovery
External interference
Latent variables
Temporal data
Variational inference
Latent variables
Temporal data
Variational inference
External interference
Causal discovery
ISSN:0925-2312
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Shrnutí:Inferring causal relationships from observed data is an important task, yet it becomes challenging when the data is subject to various external interferences. Most of these interferences are the additional effects of external factors on observed variables. Since these external factors are often unknown, we introduce latent variables to represent these unobserved factors that affect the observed data. Specifically, to capture the causal strength and adjacency information, we propose a new temporal latent variable structural causal model, incorporating causal strength and adjacency coefficients that represent the causal relationships between variables. Considering that expert knowledge can provide information about unknown interferences in certain scenarios, we develop a method that facilitates the incorporation of prior knowledge into parameters learning based on Variational Inference, to guide the model estimation. Experimental results demonstrate the stability and accuracy of our proposed method. •We propose a Temporal Latent Variable Structural Causal Model for causal discovery with external interferences.•We use a Hadamard product of adjacency and weight matrices to constrain model complexity.•We estimate the model using variational inference, integrating expert knowledge as a prior.
ISSN:0925-2312
DOI:10.1016/j.neucom.2025.130281