Using AI to Understand Complex Causation

Whenever something serious happens, we usually try and determine cause and effect. What was it that caused this thing to unfold the way it did? Whilst the theory is nice, we often employ some rather dubious explanations to try and explain the series of events. Superstitions perhaps, or correlation rather than causation. There have been attempts in the past to generate mathematical models for general causality, but they haven't been particularly effective, especially for more complex problems. A new study from the University of Johannesburg, South Africa and National Institute of Technology Rourkela, India, has attempted to use AI to do a better job.

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This article is related to

machine learning,artificial intelligence,deep learning,causal analysis,dag,manm,multivariate additive noise model