Equivalent Causal Models

No. 7: AAAI-21 Technical Tracks 7

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 35

AAAI Technical Track on Knowledge Representation and Reasoning

The aim of this paper is to offer the first systematic exploration and definition of equivalent causal models in the context where both models are not made up of the same variables. The idea is that two models are equivalent when they agree on all "essential" causal information that can be expressed using their common variables. I do so by focussing on the two main features of causal models, namely their structural relations and their functional relations. In particular, I define several relations of causal ancestry and several relations of causal sufficiency, and require that the most general of these relations are preserved across equivalent models.

10.1609/aaai.v35i7.16771