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A psychological approach to learning causal networks

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  • Manaf Zargoush
  • Farrokh Alemi
  • Vinzenzo Esposito Vinzi
  • Jee Vang
  • Raya Kheirbek

Abstract

We examine the role of a common cognitive heuristic in unsupervised learning of Bayesian probability networks from data. Human beings perceive a larger association between causal than diagnostic relationships. This psychological principal can be used to orient the arcs within Bayesian networks by prohibiting the direction that is less predictive. The heuristic increased predictive accuracy by an average of 0.51 % percent, a small amount. It also increased total agreement between different network learning algorithms (Max Spanning Tree, Taboo, EQ, SopLeq, and Taboo Order) by 25 %. Prior to use of the heuristic, the multiple raters Kappa between the algorithms was 0.60 (95 % confidence interval, CI, from 0.53 to 0.67) indicating moderate agreement among the networks learned through different algorithms. After the use of the heuristic, the multiple raters Kappa was 0.85 (95 % CI from 0.78 to 0.92). There was a statistically significant increase in agreement between the five algorithms (alpha > 0.05). These data suggest that the heuristic increased agreement between networks learned through use of different algorithms, without loss of predictive accuracy. Additional research is needed to see if findings persist in other data sets and to explain why a heuristic used by humans could improve construct validity of mathematical algorithms. Copyright Springer Science+Business Media New York 2014

Suggested Citation

  • Manaf Zargoush & Farrokh Alemi & Vinzenzo Esposito Vinzi & Jee Vang & Raya Kheirbek, 2014. "A psychological approach to learning causal networks," Health Care Management Science, Springer, vol. 17(2), pages 194-201, June.
    • Handle: RePEc:kap:hcarem:v:17:y:2014:i:2:p:194-201
    DOI: 10.1007/s10729-013-9250-2
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    References listed on IDEAS

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    1. Konstantinos V. Katsikopoulos, 2011. "Psychological Heuristics for Making Inferences: Definition, Performance, and the Emerging Theory and Practice," Decision Analysis, INFORMS, vol. 8(1), pages 10-29, March.
    2. Justin Goodson & Wooseung Jang, 2008. "Assessing nursing home care quality through Bayesian networks," Health Care Management Science, Springer, vol. 11(4), pages 382-392, December.
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    Cited by:

    1. Farrokh Alemi & Manaf Zargoush & Jee Vang, 2017. "Using observed sequence to orient causal networks," Health Care Management Science, Springer, vol. 20(4), pages 590-599, December.

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