A novel approach of causality matrix embedded into the Graph Neural Network for the of Bitcoin

Accurately forecasting Bitcoin prices presents significant challenges due to its high volatility and the complex interactions among macroeconomic and crypto-specific variables. Traditional forecasting models often rely on correlations, which fail to capture the intrinsic causal relationships that drive price fluctuations. In this paper, we propose a novel method that integrates a Cause & Effect (C&E) Matrix within a Graph Neural Network (GNN) to explicitly model these causal dependencies. Unlike correlations, causal relationships remain relatively stable even under changing market conditions, making them more reliable for robust and interpretable forecasting. Our approach begins with causal analysis to identify the key variables influencing Bitcoin's price, after which these causal links are translated into directed graph structures. These structures allow for the extraction of spatio-temporal features via GNN, capturing the underlying dynamics of Bitcoin's price movements. Experimental results demonstrate that our C&E embedded GNN significantly improves short-term Bitcoin price forecasts compared to baseline models, highlighting the critical role of causality in enhancing prediction accuracy and model interpretability in volatile markets.