Categories |
COMPUTING
MATHEMATICS
STATISTICS
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Call for Papers |
The formal study of causality started about 300 hundred years ago with the works of the great philosophers David Hume and Immanuel Kant. Hume approached causality from an empirical perspective: the knowledge that a certain thing (the potential cause) causes or prevents another (the effect) is acquired by means of experience and without any prior knowledge. He identified 3 conditions for this to happen: temporal and spatial contiguity, precedence in time of the cause with respect to the effect and constant conjunction; i.e., the constant occurrence of the both of them. The notion of covariation summarizes such a perspective. Kant, on the other hand, focused on the notion of causal power, which refers to the knowledge that some mechanism or power can cause a certain effect. As can be inferred, this is what we refer to as prior knowledge. Of course, both approaches have their respective advantages and disadvantages. Although the former have given us important clues for capturing the essential features of causality, the latter represent a serious challenge to overcome. Since then, different disciplines such as Philosophy, Psychology, Statistics and Artificial Intelligence (AI) have studied this important phenomenon. One of AI’s main interest is to build intelligent systems capable of automatically acquiring cause-effect relationships and using causal knowledge to build better intelligent systems. |
Credits and Sources |
[1] CReW 2019 : Causal Reasoning Workshop 2019 |