Abstract
Process mining aims to provide analysts with insights, such that business processes supported by information systems can be improved. Traditionally, insights from process mining projects and techniques have been associational rather than causal, thus only describing the current state of the process, without predictive capabilities over effects of hypothetical process changes, which inherently limits business process optimisation efforts. In this paper, we introduce causal analysis for control-flow decisions taken during the execution of process models: using an event log and the structure of a process model, we (i) extract the set of decision points in the process, (ii) apply a causal discovery approach to obtain a collection of causal graphs that are consistent with the observations in the event log, (iii) extract ordered pairs of decision points between which a causal connection can be ruled out based on the temporal ordering that is implied by the process model specification, and use these to narrow down the set of possible causal graphs. This technique addresses the problem of mining dependencies, which has long been a challenge in the process discovery field. The technique has been implemented in the Visual Miner as part of the ProM framework. We illustrate the technique using examples and demonstrate its applicability on real-life logs.
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Notes
- 1.
By symmetry, also \(P(Y\mid X, Z) = P(Y \mid Z)\).
References
Adriansyah, A., Sidorova, N., van Dongen, B.F.: Cost-based fitness in conformance checking. In: ACSD, pp. 57–66. IEEE (2011)
Benjamini, Y., Hochberg, Y.: Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. Royal Stat. Soc. 57(1), 289–300 (1995)
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, pp. 129–136. IEEE (2020)
Bozorgi, Z.D., Teinemaa, I., Dumas, M., Rosa, M.L., Polyvyanyy, A.: Prescriptive process monitoring for cost-aware cycle time reduction. In: ICPM, pp. 96–103. IEEE (2021)
vanden Broucke, S.K.L.M., Weerdt, J.D.: Fodina: a robust and flexible heuristic process discovery technique. Decis. Support Syst. 100, 109–118 (2017)
Brunk, J., et al.: Cause vs. effect in context-sensitive prediction of business process instances. Inf. Syst. 95, 101635 (2021)
Choueiri, A.C., Portela Santos, E.A.: Discovery of path-attribute dependency in manufacturing environments: a process mining approach. JMS 61, 54–65 (2021)
Geiger, D., Verma, T., Pearl, J.: Identifying independence in Bayesian networks. Networks 20(5), 507–534 (1990)
Günther, C.W., Rozinat, A.: Disco: discover your processes. In: BPM Demos, vol. 940, pp. 40–44. CEUR-WS.org (2012)
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
Hsieh, C., Moreira, C., Ouyang, C.: Dice4el: interpreting process predictions using a milestone-aware counterfactual approach. In: ICPM, pp. 88–95. IEEE (2021)
Kamal, I.M., Bae, H., Utama, N.I., Yulim, C.: Data pixelization for predicting completion time of events. Neurocomputing 374, 64–76 (2020)
Leemans, S.J.J., Fahland, D.: Information-preserving abstractions of event data in process mining. Knowl. Inf. Syst. 62(3), 1143–1197 (2019). https://doi.org/10.1007/s10115-019-01376-9
Leemans, S.J.J., Fahland, D., van der Aalst, W.M.P.: Discovering block-structured process models from event logs containing infrequent behaviour. In: Lohmann, N., Song, M., Wohed, P. (eds.) BPM 2013. LNBIP, vol. 171, pp. 66–78. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-06257-0_6
Leemans, S.J.J., Fahland, D., van der Aalst, W.M.P.: Exploring processes and deviations. In: Fournier, F., Mendling, J. (eds.) BPM 2014. LNBIP, vol. 202, pp. 304–316. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-15895-2_26
Leemans, S.J.J., Poppe, E., Wynn, M.T.: Directly follows-based process mining: exploration & a case study. In: ICPM, pp. 25–32. IEEE (2019)
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.: Causality: Models, Reasoning, and Inference. Cambridge UP, Cambridge (2009)
Pesic, M., Schonenberg, H., van der Aalst, W.M.P.: DECLARE: full support for loosely-structured processes. In: EDOC, pp. 287–300. IEEE (2007)
Peters, S., et al.: Fast and accurate quantitative business process analysis using feature complete queueing models. Inf. Sys. 104, 101892 (2022)
Qafari, M.S., van der Aalst, W.: Root cause analysis in process mining using structural equation models. In: Del RÃo 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
Qafari, M.S., van der Aalst, W.M.P.: Case level counterfactual reasoning in process mining. In: Nurcan, S., Korthaus, A. (eds.) CAiSE 2021. LNBIP, vol. 424, pp. 55–63. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-79108-7_7
Qafari, M.S., van der Aalst, W.M.P.: Feature recommendation for structural equation model discovery in process mining. CoRR abs/2108.07795 (2021)
Shoush, M., Dumas, M.: Prescriptive process monitoring under resource constraints: a causal inference approach. CoRR abs/2109.02894 (2021)
Spirtes, P., Glymour, C., Scheines, R.: Causation, Prediction, and Search, 2nd edn. MIT Press, Cambridge (2000)
Stierle, M.: Exploring Cause-Effect Relationships in Process Analytics - Design, Development and Evaluation of Comprehensible, Explainable and Context-Aware Techniques. Ph.D. thesis, FAU Erlangen-Nürnberg (2021)
Sun, H., Liu, W., Qi, L., Ren, X., Du, Y.: An algorithm for mining indirect dependencies from loop-choice-driven loop structure via petri nets. IEEE TSMC (2021)
Sutrisnowati, R.A., Bae, H., Park, J., Ha, B.: Learning Bayesian network from event logs using mutual information test. In: ICSOC, pp. 356–360. IEEE (2013)
Sutrisnowati, R.A., Bae, H., Song, M.: Bayesian network construction from event log for lateness analysis in port logistics. Comput. Ind. Eng. 89, 53–66 (2015)
Tax, N., Teinemaa, I., van Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. Softw. Syst. Model. 19(6), 1345–1365 (2020). https://doi.org/10.1007/s10270-020-00789-3
Tu, R., Zhang, C., Ackermann, P., Mohan, K., Kjellström, H., Zhang, K.: Causal discovery in the presence of missing data. In: AISTATS, pp. 1762–1770 (2019)
Weijters, A.J.M.M., Ribeiro, J.T.S.: Flexible heuristics miner (FHM). In: CIDM, pp. 310–317. IEEE (2011)
Wen, L., van der Aalst, W.M.P., Wang, J., Sun, J.: Mining process models with non-free-choice constructs. Data Min. Knowl. Discov. 15(2), 145–180 (2007)
van Zelst, S.J., van Dongen, B.F., van der Aalst, W.M.P., Verbeek, H.M.W.: Discovering workflow nets using integer linear programming. Computing 100(5), 529–556 (2017). https://doi.org/10.1007/s00607-017-0582-5
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Leemans, S.J.J., Tax, N. (2022). Causal Reasoning over Control-Flow Decisions in Process Models. In: Franch, X., Poels, G., Gailly, F., Snoeck, M. (eds) Advanced Information Systems Engineering. CAiSE 2022. Lecture Notes in Computer Science, vol 13295. Springer, Cham. https://doi.org/10.1007/978-3-031-07472-1_11
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