AI Definitions: Causal AI

Causal AI – This is where the principles of causal inference is applied to AI so that it uncovers connections between data points and looks for the cause-and-effect relationships to understand why things happen. Instead of predicting an outcome and its value as in predictive interference, causal inference looks at how an outcome changes if a particular factor is manipulated. While predictive AI is ideal for anticipating what a user is most likely to be interested in based on past behavior and user characteristics (such as when making purchase recommendations), causal AI will gauge the impact of changes to user behavior (such as A/B testing).

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