Abstract
Process mining is a multi-purpose tool enabling organizations to monitor and improve their processes. Process mining assists organizations to enhance their performance indicators by helping them to find and amend the root causes of performance or compliance problems. This task usually involves gathering process data from the event log and then applying some data mining and machine learning techniques. However, using the results of such techniques for process enhancement does not always lead to any process improvements. This phenomenon is often caused by mixing up correlation and causation. In this paper, we present a solution to this problem by creating causal equation models for processes, which enables us to find not only the features that cause the problem but also the effect of an intervention on any of the features. We have implemented this method as a plug-in ProM and we have evaluated it using two real and synthetic event logs. These experiments show the validity and effectiveness of the proposed method.
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Notes
- 1.
We define \(\mathbb {P}(A)\) as the set of all non-empty subsets of set A.
- 2.
- 3.
We usually use \(a \bullet b\) instead of \((a,b)\in \bullet \) for
. - 4.
If \(\leftrightarrow \ne \emptyset \), the user can restart the procedure after adding some more situation features to the causal situation specification.
.References
Buijs, J.: Receipt phase of an environmental permit application process (‘wabo’), coselog project. Eindhoven University of Technology (2014)
Chickering, D.M.: Optimal structure identification with greedy search. J. Mach. Learn. Res. 3, 507–554 (2002)
Granger, C.W.: Some recent development in a concept of causality. J. Econ. 39(1–2), 199–211 (1988)
Gupta, N., Anand, K., Sureka, A.: Pariket: mining business process logs for root cause analysis of anomalous incidents. In: Chu, W., Kikuchi, S., Bhalla, S. (eds.) DNIS 2015. LNCS, vol. 8999, pp. 244–263. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-16313-0_19
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
de Leoni, M., van der Aalst, W.M., Dees, M.: A general process mining framework for correlating, predicting and clustering dynamic behavior based on event logs. Inf. Syst. 56, 235–257 (2016). https://doi.org/10.1016/j.is.2015.07.003
Mothilal, R.K., Sharma, A., Tan, C.: Explaining machine learning classifiers through diverse counterfactual explanations. In: Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency. pp. 607–617 (2020), http://arxiv.org/abs/1905.07697
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
Ogarrio, J.M., Spirtes, P., Ramsey, J.: A hybrid causal search algorithm for latent variable models. In: Proceedings of Probabilistic Graphical Models - Eighth International Conference. pp. 368–379 (2016), http://proceedings.mlr.press/v52/ogarrio16.html
Pearl, J.: Causality. Cambridge University Press (2009)
Peters, J., Janzing, D., Schölkopf, B.: Elements of causal inference: foundations and learning algorithms. MIT press (2017)
Ratzer, A.V., et al.: CPN Tools for editing, simulating, and analysing coloured petri nets. In: van der Aalst, W.M.P., Best, E. (eds.) ICATPN 2003. LNCS, vol. 2679, pp. 450–462. Springer, Heidelberg (2003). https://doi.org/10.1007/3-540-44919-1_28
Fani Sani, M., van der Aalst, W., Bolt, A., García-Algarra, J.: Subgroup discovery in process mining. In: Abramowicz, W. (ed.) BIS 2017. LNBIP, vol. 288, pp. 237–252. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59336-4_17
Scheines, R., Spirtes, P., Glymour, C., Meek, C., Richardson, T.: The tetrad project: Constraint based aids to causal model specification. Multivar. Behav. Res. 33(1), 65–117 (1998)
Spirtes, P., Glymour, C.N., Scheines, R., Heckerman, D.: Causation, prediction, and search. MIT press (2000)
Verbeek, H., Buijs, J., Van Dongen, B., van der Aalst, W.M.: Prom 6: The process mining toolkit. Proc. of BPM Demonstration Track 615, 34–39 (2010)
Wang, Y., Liang, D., Charlin, L., Blei, D.M.: The deconfounded recommender: A causal inference approach to recommendation. CoRR abs/1808.06581 (2018), http://arxiv.org/abs/1808.06581
Zhang, J.: On the completeness of orientation rules for causal discovery in the presence of latent confounders and selection bias. Artif. Intell. 172(16–17), 1873–1896 (2008). https://doi.org/10.1016/j.artint.2008.08.001
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We thank the Alexander von Humboldt (AvH) Stiftung for supporting our research.
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Qafari, M.S., van der Aalst, W. (2020). Root Cause Analysis in Process Mining Using Structural Equation Models. In: Del Río Ortega, A., Leopold, H., Santoro, F.M. (eds) Business Process Management Workshops. BPM 2020. Lecture Notes in Business Information Processing, vol 397. Springer, Cham. https://doi.org/10.1007/978-3-030-66498-5_12
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