What would be minimal test-case(s) and corresponding solution, demonstrating curent know-how in causation/precedence analysis solving, or more simply what is it possible to achieve using causation/precedence analysis techniques since the field seems to have evolved fast during the last decade:
Statistics calculations have a cost and are often used to convince rather than used as a way to know the truth. May-be is it because actual hability of statistics to analyse facts is still limited to correlation rather than causation. SEM path analysis, went popular surprizingly in social, behavioural science and psychology, trough the use of LISREL, but seems still not be used to build survey or analyse survey results. later, Judea Pearl in 2011 and Leslie Lamport 2013 won the ACM Turing award (IT Nobel) on subjects related to causation.
Artificial intelligence needs to optimize some form of truth, using causation. But its optimisation process should be improved by including intuitive-like strategies where comfirmatory hypothese analysis would help a lot, ..."I believe". I imagine that Causation analysis is part of recent IA achievments in a way or another. But practically how ? What test-case were solved ?