Causal relationships play a fundamental role in understanding the world around us. The ability to identify and understand cause-effect relationships is critical to making informed decisions, predicting outcomes, and developing effective strategies. However, deciphering causal relationships from observational data is a difficult task, as correlations alone may not provide definitive evidence of causality. In recent years, the field of machine learning (ML) has emerged as a powerful tool for causal analysis, offering new opportunities for uncovering hidden causal mechanisms and better understanding complex systems. ML algorithms can detect patterns and dependencies in data, enabling the discovery of causal links between variables.