Causal Machine Learning

Causal Machine Learning involves using algorithms to understand cause-and-effect relationships within data. Unlike traditional models that find patterns or correlations, causal methods aim to determine whether changing one factor (like a marketing campaign) will directly influence an outcome (such as sales). This approach helps organizations make informed decisions by identifying what actions will truly lead to desired results, rather than just observing relationships. It combines statistical techniques with machine learning to uncover the underlying causes behind observed patterns, enabling more accurate predictions about the impact of interventions.