skip to main content
research-article
Public Access

Causeworks: a Mixed Initiative Framework for Causal Modeling

Published: 12 November 2022 Publication History

Abstract

The construction of computational causal models for complex systems has typically been completed manually by domain experts and is a time-consuming, cumbersome process. Operational design is a method of structured team discourse used by military planners for rapidly envisioning complex systems and relationships; however, the products are typically static diagrams on whiteboards or slides. DARPAs Causal Exploration program seeks to leverage artificial intelligence (AI) assistance and causal analytics to enable rapid system modeling and analysis. We introduce Causeworks, an application in which operators “sketch” complex systems, leverage AI tools and expert knowledge to transform the sketches into computational causal models, and then apply analytics to understand how to influence the system. We walk through human–machine collaborative model building using Causeworks and discuss feedback and lessons learned about how to flexibly apply causal modeling and thinking for expert planners that are novice modelers.

References

[1]
Jonassen DH and Ionas IG Designing effective supports for causal reasoning Educ Technol Res Dev 2008 56 3 287-308
[2]
Keim D, Kohlhammer J, Ellis G, Mansmann F. Mastering the information age - solving problems with visual analytics. Eurographics Assoc, 2010.
[3]
Sedig K, Parsons P, Dittmer M, and Ola O Beyond information access: Support for complex cognitive activities in public health informatics tools Online J Public Health Inf 2012
[4]
Pearl J Causal inference in statistics: an overview Stat Surv 2009 3 96-146
[5]
Kapler T, Gray DWS, Vasquez H, Wright W. CauseWorks: a framework for transforming user hypotheses into a computational causal model. In: VISIGRAPP (3: IVAPP), 2021; pp. 50–63.
[6]
Husain F, Proulx P, Chang M-W, Romero-Gómez R, Vasquez H. A mixed-initiative visual analytics approach for qualitative causal modeling. In: 2021 IEEE Visualization Conference (VIS), 2021; pp. 121–25.
[7]
Rittel HWJ and Webber MM Dilemmas in a general theory of planning Policy Sci 1973 4 2 155-169
[8]
Gil Y et al. Intelligent systems for geosciences: an essential research agenda Commun ACM 2018 62 1 76-84
[9]
Wirtz JJ Life in the ‘Gray Zone’: observations for contemporary strategists Def Secur Anal 2017 33 2 106-114
[10]
Butland B et al. Tackling obesities: future choices-project report 2007 Citeseer
[11]
[12]
ATP 5-0.1 Army design methodology. HQ Dept Army, 2015.
[13]
Das TK and Teng B Cognitive biases and strategic decision processes: an integrative perspective J Manag Stud 1999 36 6 757-778
[14]
McPherson K, Marsh T, and Brown M Foresight report on obesity Lancet 2007 370 9601 1755
[15]
Shih PC, Nguyen DH, Hirano SH, Redmiles DF, Hayes GR. GroupMind: supporting idea generation through a collaborative mind-mapping tool. In: Proceedings of the ACM 2009 International Conference on Supporting group work, 2009; pp. 139–48.
[16]
Chen T-J and Krishnamurthy VR Investigating a mixed-initiative workflow for digital mind-mapping J Mech Des 2020 142 10
[17]
Chen T-J, Subramanian SG, Krishnamurthy VR. Qcue: Queries and cues for computer-facilitated mind-mapping. In Proceedings of Graphics Interface 2020, GI 2020, Canadian Human-Computer Communications Society / Societ´ e c, 2020.
[18]
Chen M et al. From data analysis and visualization to causality discovery Computer (Long. Beach. Calif) 2011 44 10 84-87
[19]
Krueger R, Tremel T, Thom D. VESPa 2.0: data-driven behavior models for visual analytics of movement sequences. In: 2017 International Symposium on big data visual analytics (BDVA), 2017; pp. 1–8.
[20]
Lu Y, Wang H, Landis S, and Maciejewski R A visual analytics framework for identifying topic drivers in media events IEEE Trans Vis Comput Graph 2017 24 9 2501-2515
[21]
Wang J, Mueller K. Visual causality analysis made practical. In: 2017 IEEE Conference on visual analytics science and technology (VAST), 2017; pp. 151–161.
[22]
Wright W, Kapler T. Challenges in visualizing complex causality characteristics. In: Proc. IEEE Pacific Vis., 2018.
[23]
Schmitt J A systemic concept for operational Design 2006 US MC Warfighting Lab.
[24]
Box GEP. Robustness in the strategy of scientific model building. In: Launer R, Wilderson G, editors. Robustness in statistics. New York: Academic Press; 1979. pp. 201–236.
[25]
Kapler T, Gray DWS, Marie Vasquez H, Wright W Causeworks collaboration: simultaneous causal model construction and analysis. In: Extended Abstracts of the 2021 CHI Conference on human factors in computing systems, 2021; p. 1–6.
[26]
Wickens CD, Helton WS, Hollands JG, and Banbury S Engineering psychology and human performance 2021 Routledge
[27]
Choudhry A et al. Once upon a time in visualization: Understanding the use of textual narratives for causality IEEE Trans Vis Comput Graph 2020 27 2 1332-1342
[28]
Harboe G, Huang EM. Real-world affinity diagramming practices: Bridging the paper-digital gap. In: Proceedings of the 33rd Annual ACM Conference on human factors in computing systems, 2015; p. 95–104.
[29]
Fu W-T and Gray WD Suboptimal tradeoffs in information seeking Cogn Psychol 2006 52 3 195-242
[30]
Robinson S Simulation: the practice of model development and use 2014 Bloomsbury Publishing
[31]
Chwif L, Barretto MRP, Paul RJ. On simulation model complexity. In: 2000 Winter Simulation Conference proceedings (Cat. No. 00CH37165), 2000; vol. 1, pp. 449–55.
[32]
McCandless D Information is beautiful 2012 London Collins
[33]
Brewer CA Guidelines for selecting colors for diverging schemes on maps Cartogr J 1996 33 2 79-86

Cited By

View all

Recommendations

Comments

Information & Contributors

Information

Published In

cover image SN Computer Science
SN Computer Science  Volume 4, Issue 1
Dec 2022
1427 pages

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 12 November 2022
Accepted: 11 October 2022
Received: 30 September 2021

Author Tags

  1. Causality analysis
  2. Visual analytics
  3. User-driven modeling
  4. Artificial intelligence

Qualifiers

  • Research-article

Funding Sources

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 0
    Total Downloads
  • Downloads (Last 12 months)0
  • Downloads (Last 6 weeks)0
Reflects downloads up to 21 Oct 2024

Other Metrics

Citations

Cited By

View all

View Options

View options

Get Access

Login options

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media