Causal AI

One of the problematic aspects of ML is that most of it is completely correlational, not causal. ML algorithms work primarily by applying statistical inference and reasoning using a series of variables and a target. In essence, it tries to identify whether variable $A$ occurs with target $B$. If so, it may be that “$A$ predicts $B$.” However, there are many reasons why $A$ is predictive of $B$. One particularly dangerous outcome is when there’s a lurking variable present in the data. A lurking variable is an unreported variable $(C)$ that makes it seem like ...