mlsanityData to 13 Oct 2026

Four Guiding Principles for Modeling Causal Domain Knowledge: A Case Study on Brainstorming Approaches for Urban Blight Analysis

arXiv 2412.02400 ↗First posted 3 Dec 2024cs.CE cs.CLcited by 0

Not on the map. Why?

Abstract (arXiv)

Urban blight is a problem of high interest for planning and policy making. Researchers frequently propose theories about the relationships between urban blight indicators, focusing on relationships reflecting causality. In this paper, we improve on the integration of domain knowledge in the analysis of urban blight by introducing four rules for effective modeling of causal domain knowledge. The findings of this study reveal significant deviation from causal modeling guidelines by investigating cognitive maps developed for urban blight analysis. These findings provide valuable insights that will inform future work on urban blight, ultimately enhancing our understanding of urban blight complex interactions.

Key facts. No citing papers in the map yet (data through 13 Oct 2026). Builds on 1 of its 37 references that are in the map. First posted on arXiv 3 Dec 2024.

Builds on

1 of 37 references.

References · 1

  1. 2018Theoretical Impediments to Machine Learning With Seven Sparks from the Causal Revolutioncited by 66

Built on by

Not cited yet.