Causal AI is an approach to artificial intelligence that focuses on understanding cause-and-effect relationships rather than only identifying patterns in data. It uses causal models to examine what happens when a particular factor changes, helping systems distinguish between events that merely occur together and those where one event influences another.
This makes Causal AI useful for decision-making and scenario analysis. For example, a business can use it to estimate whether changing a product price actually affects customer demand, rather than assuming a correlation between the two. Applications include healthcare research, finance, marketing, supply-chain planning, and industrial operations, often alongside technologies such as machine learning, causal inference, and predictive analytics
