Abstract
Process mining is widely used to diagnose processes and uncover performance and compliance problems. It is also possible to see relations between different behavioral aspects, e.g., cases that deviate more at the beginning of the process tend to get delayed in the later part of the process. However, correlations do not necessarily reveal causalities. Moreover, standard process mining diagnostics do not indicate how to improve the process. This is the reason we advocate the use of structural equation models and counterfactual reasoning. We use results from causal inference and adapt these to be able to reason over event logs and process interventions. We have implemented the approach as a ProM plug-in and have evaluated it on several data sets.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bozorgi, Z.D., Teinemaa, I., Dumas, M., Rosa, M.L., Polyvyanyy, A.: Process mining meets causal machine learning: discovering causal rules from event logs. In: ICPM (2020)
Ferreira, D.R., Vasilyev, E.: Using logical decision trees to discover the cause of process delays from event logs. Comput. Ind. 70, 194–207 (2015)
Hompes, B.F.A., Maaradji, A., La Rosa, M., Dumas, M., Buijs, J.C.A.M., van der Aalst, W.M.P.: Discovering causal factors explaining business process performance variation. In: Dubois, E., Pohl, K. (eds.) CAiSE 2017. LNCS, vol. 10253, pp. 177–192. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59536-8_12
Narendra, T., Agarwal, P., Gupta, M., Dechu, S.: Counterfactual reasoning for process optimization using structural causal models. In: Hildebrandt, T., van Dongen, B.F., Röglinger, M., Mendling, J. (eds.) BPM 2019. LNBIP, vol. 360, pp. 91–106. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-26643-1_6
Pearl, J., et al.: Models, Reasoning and Inference. Cambridge University Press, Cambridge (2000)
Qafari, M.S., van der Aalst, W.: Root cause analysis in process mining using structural equation models. In: Del Río Ortega, A., Leopold, H., Santoro, F.M. (eds.) BPM 2020. LNBIP, vol. 397, pp. 155–167. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-66498-5_12
Reddix-Smalls, B.: Credit scoring and trade secrecy: an algorithmic quagmire or how the lack of transparency in complex financial models scuttled the finance market. UC Davis Bus. LJ 12, 87 (2011)
Russell, C.: Efficient search for diverse coherent explanations. In: Proceedings of the Conference on Fairness, Accountability, and Transparency, pp. 20–28 (2019)
Suriadi, S., Ouyang, C., van der Aalst, W.M.P., ter Hofstede, A.H.M.: Root cause analysis with enriched process logs. In: La Rosa, M., Soffer, P. (eds.) BPM 2012. LNBIP, vol. 132, pp. 174–186. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36285-9_18
Wachter, S., Mittelstadt, B., Russell, C.: Counterfactual explanations without opening the black box: automated decisions and the GDPR. Harv. JL Tech. 31, 841 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Qafari, M.S., van der Aalst, W.M.P. (2021). Case Level Counterfactual Reasoning in Process Mining. In: Nurcan, S., Korthaus, A. (eds) Intelligent Information Systems. CAiSE 2021. Lecture Notes in Business Information Processing, vol 424. Springer, Cham. https://doi.org/10.1007/978-3-030-79108-7_7
Download citation
DOI: https://doi.org/10.1007/978-3-030-79108-7_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-79107-0
Online ISBN: 978-3-030-79108-7
eBook Packages: Computer ScienceComputer Science (R0)