Skip to main content

Advertisement

SpringerLink
Log in

Causeworks: a Mixed Initiative Framework for Causal Modeling

  • Original Research
  • Published:
SN Computer Science Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Jonassen DH, Ionas IG. Designing effective supports for causal reasoning. Educ Technol Res Dev. 2008;56(3):287–308.

    Article  Google Scholar 

  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, Ola O. Beyond information access: Support for complex cognitive activities in public health informatics tools. Online J Public Health Inf. 2012. https://doi.org/10.5210/ojphi.v4i3.4270.

    Article  Google Scholar 

  4. Pearl J. Causal inference in statistics: an overview. Stat Surv. 2009;3:96–146.

    Article  MathSciNet  MATH  Google Scholar 

  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, Webber MM. Dilemmas in a general theory of planning. Policy Sci. 1973;4(2):155–69.

    Article  Google Scholar 

  8. Gil Y, et al. Intelligent systems for geosciences: an essential research agenda. Commun ACM. 2018;62(1):76–84.

    Article  Google Scholar 

  9. Wirtz JJ. Life in the ‘Gray Zone’: observations for contemporary strategists. Def Secur Anal. 2017;33(2):106–14.

    Article  Google Scholar 

  10. Butland B, et al. Tackling obesities: future choices-project report, vol. 10. Citeseer; 2007.

    Google Scholar 

  11. DARPA. No Title. https://www.darpa.mil/program/causalexploration. Accessed 08 Aug 2022

  12. ATP 5-0.1 Army design methodology. HQ Dept Army, 2015.

  13. Das TK, Teng B. Cognitive biases and strategic decision processes: an integrative perspective. J Manag Stud. 1999;36(6):757–78.

    Article  Google Scholar 

  14. McPherson K, Marsh T, Brown M. Foresight report on obesity. Lancet. 2007;370(9601):1755.

    Article  Google Scholar 

  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, Krishnamurthy VR. Investigating a mixed-initiative workflow for digital mind-mapping. J Mech Des. 2020;142(10): 101404.

    Article  Google Scholar 

  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):84–7.

    Google Scholar 

  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, Maciejewski R. A visual analytics framework for identifying topic drivers in media events. IEEE Trans Vis Comput Graph. 2017;24(9):2501–15.

    Article  Google Scholar 

  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. US MC Warfighting Lab.; 2006.

    Google Scholar 

  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, Banbury S. Engineering psychology and human performance. Routledge; 2021.

    Book  Google Scholar 

  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):1332–42.

    Article  Google Scholar 

  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, Gray WD. Suboptimal tradeoffs in information seeking. Cogn Psychol. 2006;52(3):195–242.

    Article  Google Scholar 

  30. Robinson S. Simulation: the practice of model development and use. Bloomsbury Publishing; 2014.

    Book  Google Scholar 

  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. London: Collins; 2012.

    Google Scholar 

  33. Brewer CA. Guidelines for selecting colors for diverging schemes on maps. Cartogr J. 1996;33(2):79–86.

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the Defense Advanced Research Projects Agency (DARPA) under Contract Number FA8650-17-C-7720. The views, opinions, and findings contained in this report are those of the authors and should not be construed as an official Department of Defense position, policy, or decision. This work has been approved for Public Release, Distribution Unlimited. The authors wish to thank all Causal Exploration collaborators for their contributions and specially thank Program Managers Steve Jameson and Joshua Elliot for their inspiring vision and leadership.

Funding

This work was supported by the Defense Advanced Research Projects Agency (DARPA) under Contract Number FA8650-17-C-7720. The views, opinions, and findings contained in this report are those of the authors and should not be construed as an official Department of Defense position, policy, or decision. This work has been approved for public release, distribution unlimited.

Author information

Authors and Affiliations

  1. Uncharted Software, Toronto, ON, Canada

    Thomas Kapler, Derek Gray, Holland Vasquez & William Wright

Authors
  1. Thomas Kapler
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Derek Gray
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Holland Vasquez
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. William Wright
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Holland Vasquez.

Ethics declarations

Conflict of Interest

All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non–financial interest in the subject matter or materials discussed in this manuscript. The authors have no financial or proprietary interests in any material discussed in this article.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the topical collection “Computer Vision, Imaging and Computer Graphics Theory and Applications” guest edited by Jose Braz, A. Augusto Sousa, Alexis Paljic, Christophe Hurter and Giovanni Maria Farinella.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kapler, T., Gray, D., Vasquez, H. et al. Causeworks: a Mixed Initiative Framework for Causal Modeling. SN COMPUT. SCI. 4, 54 (2023). https://doi.org/10.1007/s42979-022-01452-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s42979-022-01452-y

Keywords

Search

Navigation