Temporal Latent Variable Structural Causal Model for Causal Discovery under External Interferences

TL;DR

This paper introduces a novel temporal latent variable structural causal model that accounts for external interferences in causal discovery, utilizing variational inference and prior knowledge to improve stability and accuracy.

Contribution

It proposes a new causal discovery model with latent variables and causal coefficients, integrating expert knowledge and variational inference for better handling external interferences.

Findings

Model demonstrates improved stability in causal inference.

Method achieves higher accuracy compared to existing approaches.

Incorporating prior knowledge guides parameter estimation effectively.

Abstract

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 parameter learning based on Variational Inference, to guide the model estimation. Experimental results demonstrate the stability and accuracy of our proposed method.