CauseWorks: A Framework for Transforming User Hypotheses into a Computational Causal Model

Thomas Kapler, Derek Gray, Holland Vasquez, William Wright

Abstract

Causal Model building for complex problems has typically been completed manually by domain experts and is a time-consuming, cumbersome process. Operational Design defines a process of rapid, structured discourse for teams to envision systems and relationships about complex, “wicked” problems, however, the resulting models are simple diagrams produced on whiteboards or slides, and as such, do not support computational analytics, thus limiting usefulness. We introduce CauseWorks, an application that helps operators “sketch” complex systems and transforms sketches into computational causal models using automatic and semiautomatic causal model construction from knowledge extracted from unstructured and structured documents. CauseWorks then provides computational analytics to assist users in understanding and influencing the system. We walk through human-machine collaborative model-building with CauseWorks and describe its application to regional conflict scenarios. We discuss feedback from subject matter experts as well as lessons learned.

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Paper Citation


in Harvard Style

Kapler T., Gray D., Vasquez H. and Wright W. (2021). CauseWorks: A Framework for Transforming User Hypotheses into a Computational Causal Model.In Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: IVAPP, ISBN 978-989-758-488-6, pages 50-63. DOI: 10.5220/0010194300500063


in Bibtex Style

@conference{ivapp21,
author={Thomas Kapler and Derek Gray and Holland Vasquez and William Wright},
title={CauseWorks: A Framework for Transforming User Hypotheses into a Computational Causal Model},
booktitle={Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: IVAPP,},
year={2021},
pages={50-63},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010194300500063},
isbn={978-989-758-488-6},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: IVAPP,
TI - CauseWorks: A Framework for Transforming User Hypotheses into a Computational Causal Model
SN - 978-989-758-488-6
AU - Kapler T.
AU - Gray D.
AU - Vasquez H.
AU - Wright W.
PY - 2021
SP - 50
EP - 63
DO - 10.5220/0010194300500063