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Causal AI, a burgeoning field within artificial intelligence, focuses on understanding cause-and-effect relationships to improve decision-making.

Unlike traditional AI which relies on correlations, causal AI uses techniques like causal graphs and Bayesian networks to identify how variables influence outcomes, enabling more effective interventions and predictions.

While offering significant benefits across industries, including healthcare, marketing, and finance, its adoption faces challenges such as data requirements and the need for specialised expertise. However, advancements in machine learning and a growing number of vendors are driving its increasing accessibility and use.