Abstract
Business processes are complex and involve the execution of various steps using different resources that can be shared across various tasks. Processes require analysis and process owners need to constantly look for methods to improve process performance indicators. It is non-trivial to quantify the improvement of a proposed change, without implementing or conducting randomized controlled trials. In several cases, the cost and time for implementing and evaluating the benefits of these changes are high. To address this, we propose a principled framework using Structural Causal Models which formally codify existing cause-effect assumptions about the process, control confounding and answer “what if” questions with observational data. We formally define an end to end methodology which takes process execution logs and specified BPMN model as inputs for structural causal model discovery and for performing counterfactual reasoning. We show that exploiting the process specification for causal discovery automatically ensures the inclusion of subject matter expertise, and also provides an effective computational methodology. We illustrate the effectiveness of our approach by answering intervention and counterfactual questions on example process models.
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Notes
- 1.
- 2.
Although there are several open datasets for business processes, to verify the correctness of a causal model, intervention data is required. To the best of our knowledge, there is no existing dataset which documents the effect of multiple interventions in the business process domain. Hence, we use simulated data to showcase the feasibility of our approach.
- 3.
We tried three variants of linear regression - without regularization, and with L1 and L2 regularization. Empirically, the statistical model without regularization gave the best performance.
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Narendra, T., Agarwal, P., Gupta, M., Dechu, S. (2019). Counterfactual Reasoning for Process Optimization Using Structural Causal Models. In: Hildebrandt, T., van Dongen, B., Röglinger, M., Mendling, J. (eds) Business Process Management Forum. BPM 2019. Lecture Notes in Business Information Processing, vol 360. Springer, Cham. https://doi.org/10.1007/978-3-030-26643-1_6
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