Causal Inference (Glossary)

Causal inference is an important term in the fields of artificial intelligence, big data, smart data and digital transformation. It describes methods used to recognise relationships between cause and effect. Unlike simple data analyses, which only find correlations, causal inference is about finding out whether something really influences another thing.

Imagine an online shop notices that customers who receive a discount offer make more purchases. Thanks to causal inference, it is possible to find out whether the discount offer actually causes more sales - or whether there are other reasons why these customers are more likely to buy anyway. This enables well-founded decisions to be made: If there is a clear correlation, it pays to work with targeted discounts.

Causal inference turns data into real knowledge by finding not just clues, but real causes. In this way, companies can optimise their processes, make marketing measures more effective or better assess the success of new products. In a data-driven world in particular, causal inference provides a valuable advantage for not only taking action, but also taking the right measures in a targeted manner.