Bridging the Unavoidable A Priori: A Framework for Comparative Causal Modeling

TL;DR

This paper proposes a unified mathematical framework combining system dynamics and structural equation modeling to improve causal understanding in AI/ML, addressing the challenge of integrating different assumptions.

Contribution

It introduces a novel framework that bridges system dynamics and structural equation modeling for causal analysis in AI/ML.

Findings

Unified framework for causal modeling

Enhanced comparison of causal models

Improved understanding of system dynamics in data science

Abstract

AI/ML models have rapidly gained prominence as innovations for solving previously unsolved problems and their unintended consequences from amplifying human biases. Advocates for responsible AI/ML have sought ways to draw on the richer causal models of system dynamics to better inform the development of responsible AI/ML. However, a major barrier to advancing this work is the difficulty of bringing together methods rooted in different underlying assumptions (i.e., Dana Meadow's "the unavoidable a priori"). This paper brings system dynamics and structural equation modeling together into a common mathematical framework that can be used to generate systems from distributions, develop methods, and compare results to inform the underlying epistemology of system dynamics for data science and AI/ML applications.