Causation to Provide the Why

Basic causation is a great start.   Why did this happen? We are doing it all the time.  Its one of our basic knowledge processing capabilities that lead to learning.    We can figure out the answer by observation and combing observations to build rules of operation.   Or we can be taught specific rules, or even  imprecise rules of thumb, to help us process knowledge.   Combining things we have observed or not.   They must include things like causation, space and time relationships.  It is this kind of knowledge we need to do general AI.  Not just more data.   Its more than just unstructured data.  Its about combining all learning experiences we experience into an interacting data rich architecture that we can use.   Like the direction of the below:

An AI Pioneer Wants His Algorithms to Understand the 'Why'
  Will Knight, in Wired

Yoshua Bengio, a researcher at the University of Montreal in Canada who is co-recipient of the 2018 ACM A.M. Turing Award for contributions to the development of deep learning, thinks artificial intelligence will not realize its full potential until it can move beyond pattern recognition and learn more about cause and effect, which would make existing AI systems smarter and more efficient. A robot that understands dropping things causes them to break, for example, would not need to toss dozens of vases onto the floor to see what happens to them. Bengio is developing a version of deep learning that can recognize simple cause-and-effect relationships. His team used a dataset that maps causal relationships between real-world phenomena in terms of probabilities. The resulting algorithm essentially forms a hypothesis about which variables are causally related, and then tests how changes to different variables fit the theory.  ... "