Causal Machine Learning is a term from the categories of Artificial Intelligence, Big Data and Smart Data, and Digital Transformation. In contrast to conventional machine learning, which merely identifies correlations, Causal Machine Learning aims to discover genuine cause-and-effect relationships.<\/p>\n
This means: Instead of just analysing that people who frequently buy sportswear also often book fitness classes, Causal Machine Learning attempts to find out whether buying sportswear really leads to someone later booking classes \u2013 or whether both things might be influenced by another factor such as health consciousness.<\/p>\n
A vivid example: A company wants to know if a particular advertising campaign actually leads to more sales. Traditional methods only show a correlation. Causal Machine Learning, however, checks whether the campaign is truly the cause of the sales increase \u2013 or if there are other reasons behind it, such as a general trend movement.<\/p>\n
This provides companies, especially decision-makers, with a better basis for important business decisions and allows them to invest more targetedly in measures that demonstrably have a positive effect. Causal Machine Learning thus ensures greater clarity and security in a data-driven world.<\/p>","protected":false},"excerpt":{"rendered":"