# Learning causation event conjunction sequences

arXiv: https://arxiv.org/abs/2402.14027 · HTML: https://mlsanity.com/paper/2402.14027/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 11 references that are in the map.
- First posted on arXiv 17 Feb 2024.

## Builds on (most cited first, 3 of 3)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Neural Turing Machines](https://mlsanity.com/paper/1410.5401/) (2014)
- [Order Matters: Sequence to sequence for sets](https://mlsanity.com/paper/1511.06391/) (2015)

## Abstract (arXiv)

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.