Knowledge models for causal analysis, using a strongly typed database in TypeDB. Better understanding the consequences and immediate causes of given situations.
Join the DZone community and get the full member experience. A new term is floating around the Computer Science Artificial Intelligence circles that is catching on — “Causal Science” — and it seems this technique helps us better predict future behaviors. The father of Causal Science is none other than Judea Pearl, the same Judea Pearl that created Bayesian Networks. His work on Bayesian networks and causation was so profound that in 2011 Professor Pearl was awarded the highest honors in both Computer Science and Human Cognition. He was awarded the Allen Turing Award “for fundamental contributions to artificial intelligence through the development of a calculus for probabilistic and causal reasoning”. Additionally in the same year, he received the Rumelhart Prize for Contributions to the theoretical foundation of human cognition. When someone, in the same year, wins an award for contributions to theoretical foundations of Human Cognition and the equivalent to the Nobel prize for Computer Science, that person is probably on to something, and we should pay attention. On a more lighthearted note, Pearl may indeed be the real-life version of the science fiction character Hari Seldon. Hari Seldon is the mastermind of the “Seldon plan” in the Isaac Asimov novel “ Foundation ” (coming soon to Apple TV). In the book, Hari Seldon creates a new branch of mathematics called psychohistory which Asimov defines as — “Psychohistory depends on the idea that, while one cannot foresee the actions of a particular individual, the laws of statistics as applied to large groups of people could predict the general flow of future events.“ One of my favorite definitions of causal science is “Causality is the study of how things influence one other, how causes lead to effects.” Said another way, “what events need to happen to cause desired outcomes to occur.” Sounds like the Seldon plan:). Is real life emulating science fiction again? Only time will tell, but many big companies are placing big bets on Pearl’s Causal Science approach to artificial intelligence. Netflix, Lyft, Microsoft, and Google are all using it and a host of other companies are seeing the benefits of causal science being embedded in their algorithms. One of the hallmarks of a causal solution is explainability. Many traditional approaches in data science struggle with explaining why they made a certain recommendation. Industrial companies are cautious to implement recommendations that cannot be explained. We are even seeing lawmakers pass Right to Explanation legislation around automated decision-making.
