Equivalent Causal Models
December 10, 2020 Β· Declared Dead Β· π AAAI Conference on Artificial Intelligence
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Authors
Sander Beckers
arXiv ID
2012.05603
Category
cs.AI: Artificial Intelligence
Citations
9
Venue
AAAI Conference on Artificial Intelligence
Last Checked
4 months ago
Abstract
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.
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