Do Not Marginalize Mechanisms, Rather Consolidate!
This addresses the need for efficient causal analysis in machine learning and AI as systems grow more complex, though it appears incremental by building on existing marginalization methods.
The paper tackles the problem of simplifying large-scale structural causal models (SCMs) without destroying causality, by introducing consolidation of causal mechanisms to preserve consistent interventional behavior, and discusses computational complexity reduction and generalization abilities.
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.