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

arXiv: https://arxiv.org/abs/2511.21636 · HTML: https://mlsanity.com/paper/2511.21636/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 18 references that are in the map.
- First posted on arXiv 26 Nov 2025.

## Builds on (most cited first, 2 of 2)

- [Advancing Community Engaged Approaches to Identifying Structural Drivers of Racial Bias in Health Diagnostic Algorithms](https://mlsanity.com/paper/2305.13485/) (2023)
- [Loop Polarity Analysis to Avoid Underspecification in Deep Learning](https://mlsanity.com/paper/2309.10211/) (2023)

## Abstract (arXiv)

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.