Skip to main content

Causal Interventions and Argumentation-Based Frameworks: Formalization of “What If” Scenarios

  • Conference paper
  • First Online:
Highlights in Practical Applications of Agents, Multi-Agent Systems, and Social Good. The PAAMS Collection (PAAMS 2021)

Abstract

Argumentation-based frameworks are used as a decision-making mechanism for software agents. This paper aims to investigate how a formal argumentation framework is affected when the underlying causal relationships of its theory is modified in counterfactual situations, the so-called “what if” scenarios. In contrast to previous approaches where causality relationships were derived from static probabilistic distributions, we address scenarios where causal models are intervened. Two novel contributions in the synergy between argumentation and causal theories are presented: 1) we characterize interventions and their consequences in causal argumentation frameworks; and 2) we introduce an account of the so-called sequential interventions that give a characterization of manipulations on time.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. DoWhy: A Python package for causal inference. https://github.com/microsoft/dowhy

  2. Amgoud, L.: Postulates for logic-based argumentation systems. Int. J. Approx. Reason. 55(9), 2028–2048 (2014)

    Article  MathSciNet  Google Scholar 

  3. Amgoud, L., Cayrol, C.: Inferring from inconsistency in preference-based argumentation frameworks. J. Autom. Reason. 29(2), 125–169 (2002)

    Article  MathSciNet  Google Scholar 

  4. Arjas, E., Parner, J.: Causal reasoning from longitudinal data. Scand. J. Stat. 31(2), 171–187 (2004)

    Article  MathSciNet  Google Scholar 

  5. Dung, P.M.: On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n-person games. Artif. Intell. 77(2), 321–357 (1995)

    Article  MathSciNet  Google Scholar 

  6. Gabbay, D.M., Rodrigues, O.: Probabilistic argumentation: an equational approach. Log. Univers. 9(3), 345–382 (2015)

    Article  MathSciNet  Google Scholar 

  7. Galles, D., Pearl, J.: Axioms of causal relevance. Artif. Intell. 97(1–2), 9–43 (1997)

    Article  MathSciNet  Google Scholar 

  8. Gelfond, M., Lifschitz, V.: Classical negation in logic programs and disjunctive databases. New Gener. Comput. 9(3–4), 365–385 (1991)

    Article  Google Scholar 

  9. Guerrero, E., Nieves, J.C., Lindgren, H.: Semantic-based construction of arguments: an answer set programming approach. Int. J. Approx. Reason. 64, 54–74 (2015)

    Article  MathSciNet  Google Scholar 

  10. Guerrero, E., Nieves, J.C., Sandlund, M., Lindgren, H.: Activity qualifiers in an argumentation framework as instruments for agents when evaluating human activity. In: Demazeau, Y., Ito, T., Bajo, J., Escalona, M.J. (eds.) PAAMS 2016. LNCS (LNAI), vol. 9662, pp. 133–144. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-39324-7_12

    Chapter  Google Scholar 

  11. Halpern, J.Y., Pearl, J.: Causes and explanations: a structural-model approach. Part II: Explanations. Br. J. Philos. Sci. 56(4), 889–911 (2005)

    Article  Google Scholar 

  12. Harel, D.: First-Order Dynamic Logic. Lecture Notes in Computer Science, vol. 68. Springer, Heidelberg (1979). https://doi.org/10.1007/3-540-09237-4

    Book  MATH  Google Scholar 

  13. Hunter, A.: A probabilistic approach to modelling uncertain logical arguments. Int. J. Approx. Reason. 54(1), 47–81 (2013)

    Article  MathSciNet  Google Scholar 

  14. Li, H., Oren, N., Norman, T.J.: Probabilistic argumentation frameworks. In: Modgil, S., Oren, N., Toni, F. (eds.) TAFA 2011. LNCS (LNAI), vol. 7132, pp. 1–16. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-29184-5_1

    Chapter  Google Scholar 

  15. Lindgren, H., et al.: The Star-C Intelligent Coach: A Crossdisciplinary Design Process of a Behaviour Change Intervention in Primary Care. Prague, Czech Republic (2020, in press)

    Google Scholar 

  16. Morveli-Espinoza, M., Nieves, J.C., Augusto Tacla, C.: Towards an imprecise probability approach for abstract argumentation. In: 24th European Conference on Artificial Intelligence (ECAI 2020), Including 10th Conference on Prestigious Applications of Artificial Intelligence (PAIS 2020), Virtual, 29 August–8 September 2020, pp. 2921–2922. IOS Press (2020)

    Google Scholar 

  17. Morveli-Espinoza, M., Nieves, J.C., Tacla, C.A.: An imprecise probability approach for abstract argumentation based on credal sets. In: Kern-Isberner, G., Ognjanović, Z. (eds.) ECSQARU 2019. LNCS (LNAI), vol. 11726, pp. 39–49. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-29765-7_4

    Chapter  Google Scholar 

  18. Ng, N., et al.: Sustainable behavior change for health supported by person-tailored, adaptive, risk-aware digital coaching in a social context: study protocol for the STAR-C research programme. Front. Public Health 9, 138 (2021)

    Google Scholar 

  19. Norberg, M., Wall, S., Boman, K., Weinehall, L.: The Västerbotten Intervention Programme: background, design and implications. Glob. Health Action 3(1), 4643 (2010)

    Article  Google Scholar 

  20. Pearl, J.: Causal diagrams for empirical research. Biometrika 82(4), 669–688 (1995)

    Article  MathSciNet  Google Scholar 

  21. Pearl, J.: Probabilities of causation: three counterfactual interpretations and their identification. Synthese 121(1–2), 93–149 (1999)

    Article  MathSciNet  Google Scholar 

  22. Pearl, J.: An introduction to causal inference. Int. J. Biostat. 6(2), 1643–1662 (2010)

    Article  MathSciNet  Google Scholar 

  23. Thimm, M.: The Tweety library collection for logical aspects of artificial intelligence and knowledge representation. Künstl. Intell. 31(1), 93–97 (2017)

    Article  Google Scholar 

  24. Tian, J., Pearl, J.: Probabilities of causation: bounds and identification. Ann. Math. Artif. Intell. 28(1–4), 287–313 (2000)

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgment

Research was supported by Forte, the Swedish Research Council for Health, Working Life and Welfare, which supports the STAR-C project during 2019–2024 (Dnr. 2018-01461).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Esteban Guerrero .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Guerrero, E. (2021). Causal Interventions and Argumentation-Based Frameworks: Formalization of “What If” Scenarios. In: De La Prieta, F., El Bolock, A., Durães, D., Carneiro, J., Lopes, F., Julian, V. (eds) Highlights in Practical Applications of Agents, Multi-Agent Systems, and Social Good. The PAAMS Collection. PAAMS 2021. Communications in Computer and Information Science, vol 1472. Springer, Cham. https://doi.org/10.1007/978-3-030-85710-3_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-85710-3_13

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-85709-7

  • Online ISBN: 978-3-030-85710-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics