Data Science and machine learning are becoming a fundamental part of the human evolution. Any person you may find in the street has heard about "Artificial Intelligence", and it is unthinkable for a enterprise with a good size to do not organize and analyze its data. From economics to medicine, machine learning algorithms are showing that they are able to help in the daily life of billions of people.\
Though, most of these tools, and specially the most modern ones (CNN, LLMs, AGI), lack of an important feature. They are able to obtain, very efficiently, what they are "asked" to do, but they don't usually explain clearly why they are doing the elections.\ If a doctor wants an algorithm that predicts whether a patient needs certain medication or not, he doesn't need just the output "yes" or "no". He needs a justification that explains the reason why the algorithm made certain prediction.\
Humans are causal learners. We see events triggered by another ones, extract a rule of the kind "If A happens, then B is probably going to happen too", and extrapolate from there. It is much easier to explain to a doctor that his patient needs certain medicament "because he has these symptoms" than "because my neural network has learned these numbers as weights". Mathematics is a clear example of how far a human can get with logical causalities.\
Causal inference is able to get to that point. Its tools are capable of not simply predicting what is going to happen, but of extracting implications and using them. They are able to see an event, understand it, and predict what may happen next, having a deep understanding of why it will happen.
