Causal Inference and Counterfactual Reasoning for Stock Market Prediction: A Graph-Based State Space Approach

Abstract

Understanding causal relationships in financial markets is fundamental to developing robust stock prediction models that can reason about interventions and hypothetical scenarios. Traditional correlation-based prediction models fail to capture the underlying causal mechanisms that drive market dynamics, limiting their ability to answer counterfactual questions such as "how would this stock perform if the Federal Reserve had not raised interest rates?" This paper proposes a novel framework that integrates causal inference principles with graph-based state space modeling for enhanced stock market prediction. Our approach, Causal Stock State Space Graph (C-S3G), builds upon the Stock State Space Graph architecture introduced by Lu, Hu, and Zhang, and incorporates causal discovery techniques and counterfactual reasoning mechanisms. We adapt explanation-based bias decoupling regularization principles from Zang and Liu's work on natural language inference to distinguish genuine causal relationships from spurious correlations in financial time series. Through extensive experiments on benchmark financial datasets, we demonstrate that the proposed framework substantially outperforms correlation-based baselines in counterfactual prediction tasks while maintaining competitive accuracy in standard prediction scenarios. Our work contributes to the growing body of research on causal deep learning for finance, providing a principled approach to understanding cause-effect relationships in stock markets.