Publication
Do Not Marginalize Mechanisms, Rather Consolidate!
Moritz Willig; Matej Zecevic; Devendra Singh Dhami; Kristian Kersting
In: Computing Research Repository eprint Journal (CoRR), Vol. abs/2310.08377, Pages 1-19, arXiv, 2023.
Abstract
Structural causal models (SCMs) are a powerful tool for understanding the complex
causal relationships that underlie many real-world systems. As these systems grow
in size, the number of variables and complexity of interactions between them does,
too. Thus, becoming convoluted and difficult to analyze. This is particularly true in
the context of machine learning and artificial intelligence, where an ever increasing
amount of data demands for new methods to simplify and compress large scale
SCM. While methods for marginalizing and abstracting SCM already exist today,
they may destroy the causality of the marginalized model. To alleviate this, we
introduce the concept of consolidating causal mechanisms to transform large-scale
SCM while preserving consistent interventional behaviour. We show consolidation
is a powerful method for simplifying SCM, discuss reduction of computational
complexity and give a perspective on generalizing abilities of consolidated SCM.