Re-examining Granger Causality from Causal Bayesian Networks Perspective

The emergence of machine learning (ML) has been phenomenal, with ML-based models outperforming human intelligence, as in the case of AlphaGo [1] and, more recently, large language models (LLMs). With these advances, ML became state-of-the-art for scientific discovery in various fields of study [2]. However, ML algorithms fail to answer the crucial question "what" brings about an effect and "what if" questions i.e., ML cannot identify causal relationships in data and counterfactual questions. Hence, the need for causality and causal inference a field that focuses on unravelling causal interactions in data. Characterising these interactions in complex dynamical systems is a fundamental question in science [3]. Causal structure learning (CSL)--a computational causal discovery field, taking advantage of statistics and machine learning (ML) to unravel causal relations in data--is particularly appealing because it enables us to answer counterfactual questions [4, 5, 6, 7]. We adopt Pearl's causality framework.