Abstract
Predictive Process Monitoring (PPM) deals with providing predictions about the continuation of partially executed process executions based on historical process data. PPM techniques have been developed using increasingly complex Machine and Deep Learning architectures, which lack interpretability of the predictions. Recently, explainable PPM techniques have been proposed, thus making them more ”trustable” for the users. Amongst these techniques, counterfactuals aim at suggesting, for a given process execution, the minimal changes to be applied to it to achieve a desired outcome. In this paper, we introduce an evaluation framework for evaluating different approaches for the generation of counterfactuals in PPM. The framework is used to evaluate these approaches against several real-life datasets. The results show that, although a clear winner cannot be identified, each approach is suitable for logs with specific characteristics, or for achieving specific objectives.
This work was partially supported by the Italian (MUR) under PRIN project PINPOINT Prot. 2020FNEB27, CUP H23C22000280006 and H45E21000210001 and PNRR project FAIR-Future AI Research (PE00000013), under the NRRP MUR program funded by NextGenerationEU. The support is gratefully acknowledged.
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Notes
- 1.
The bpic2015 consists of 5 variants of the same process.
- 2.
The discovery was done on complete traces, while the sat score was computed for prefixes, where constraints may be temporarily violated but become satisfied as the execution continues.
- 3.
We used the rule mining tool RuM [2].
- 4.
The complete results per dataset and discovered Declare models are available at DiCE_results.
- 5.
Since we are considering trace prefixes, it can happen that the prefix does not satisfy a process constraint, which will, instead, be satisfied in the complete trace.
References
van der Aalst, W.M.P.: Process Mining - Data Science in Action, 2nd edn. Springer, Cham (2016)
Alman, A., Di Ciccio, C., Maggi, F.M.: Rule mining with rum (extended abstract). CEUR Workshop Proc. 2952, 38–43 (2021)
Guidotti, R., Monreale, A., Ruggieri, S., Pedreschi, D., Turini, F., Giannotti, F.: Local rule-based explanations of black box decision systems. arXiv preprint: arXiv:1805.10820 (2018)
Guidotti, R., Ruggieri, S.: Ensemble of counterfactual explainers. In: Soares, C., Torgo, L. (eds.) DS 2021. LNCS (LNAI), vol. 12986, pp. 358–368. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-88942-5_28
Hsieh, C., Moreira, C., Ouyang, C.: Dice4el: interpreting process predictions using a milestone-aware counterfactual approach. In: ICPM, pp. 88–95 (2021)
Huang, T., Metzger, A., Pohl, K.: Counterfactual explanations for predictive business process monitoring. In: Themistocleous, M., Papadaki, M. (eds.) Information Systems. EMCIS 2021. Lecture Notes in Business Information Processing, vol. 437, pp. 399–413. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-95947-0_28
Leontjeva, A., Conforti, R., Di Francescomarino, C., Dumas, M., Maggi, F.M.: Complex symbolic sequence encodings for predictive monitoring of business processes. In: Motahari-Nezhad, H.R., Recker, J., Weidlich, M. (eds.) BPM 2015. LNCS, vol. 9253, pp. 297–313. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-23063-4_21
Maggi, F.M., Di Francescomarino, C., Dumas, M., Ghidini, C.: Predictive monitoring of business processes. In: Jarke, M., et al. (eds.) CAiSE 2014. LNCS, vol. 8484, pp. 457–472. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07881-6_31
Mothilal, R.K., Sharma, A., Tan, C.: Explaining machine learning classifiers through diverse counterfactual explanations, pp. 607–617. FAT* 2020 (2020)
Pesic, M., Schonenberg, H., van der Aalst, W.M.: Declare: Full support for loosely-structured processes. In: EDOC 2007, pp. 287–287 (2007)
Stierle, M., Brunk, J., Weinzierl, S., Zilker, S., Matzner, M., Becker, J.: Bringing light into the darkness-a systematic literature review on explainable predictive business process monitoring techniques. ECIS Research-in-Progress Papers (2021)
Verma, S., Dickerson, J.P., Hines, K.: Counterfactual explanations for machine learning: a review. CoRR abs/2010.10596 (2020)
Wachter, S., Mittelstadt, B.D., Russell, C.: Counterfactual explanations without opening the black box: automated decisions and the GDPR. CoRR abs/1711.00399 (2017)
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Buliga, A., Di Francescomarino, C., Ghidini, C., Maggi, F.M. (2023). Counterfactuals and Ways to Build Them: Evaluating Approaches in Predictive Process Monitoring. In: Indulska, M., Reinhartz-Berger, I., Cetina, C., Pastor, O. (eds) Advanced Information Systems Engineering. CAiSE 2023. Lecture Notes in Computer Science, vol 13901. Springer, Cham. https://doi.org/10.1007/978-3-031-34560-9_33
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