Causal inference is a key concept in the fields of Artificial Intelligence, Big Data and Smart Data, and Digital Transformation. It describes methods used to identify relationships between cause and effect. Unlike simple data analyses, which only find correlations, causal inference aims to determine whether one thing truly influences another.<\/p>\n
Imagine an online shop notices that customers who receive a discount offer tend to shop more frequently. Causal inference can help determine whether the discount offer actually causes more sales \u2013 or whether there are other reasons why these customers are more likely to buy anyway. This leads to well-informed decisions: If there is a clear connection, it is worth specifically working with discounts.<\/p>\n
Causal inference turns data into real knowledge by identifying actual causes, not just correlations. In this way, companies can optimise their processes, make marketing campaigns more effective, or better assess the success of new products. Especially in a data-driven world, causal inference provides a valuable advantage, enabling companies not just to act, but to take precisely the right measures.<\/p>","protected":false},"excerpt":{"rendered":"