  This is an examination of some methods that learn causations in event
sequences. A causation is defined as a conjunction of one or more cause events
occurring in an arbitrary order, with possible intervening non-causal events,
that lead to an effect. The methods include recurrent and non-recurrent
artificial neural networks (ANNs), as well as a histogram-based algorithm. An
attention recurrent ANN performed the best of the ANNs, while the histogram
algorithm was significantly superior to all the ANNs.
