Discovering Causal Factors Explaining Business Process Performance Variation

Hompes, Bart F. A.; Maaradji, Abderrahmane; Rosa, Marcello La; Dumas, Marlon; Buijs, Jcam Joos; Aalst, Wil M. P. van der

doi:10.1007/978-3-319-59536-8_12

Cited by 39 publications

(18 citation statements)

References 18 publications

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“…Causal inference has been applied in several ways in the field of process mining. Hompes et al [8] propose a time-series-based approach to discover cause-effect relations between aggregate characteristics of a process (e.g. the frequency of an activity) and a process performance indicator (e.g.…”

Section: Related Workmentioning

confidence: 99%

Prescriptive Process Monitoring for Cost-Aware Cycle Time Reduction

Bozorgi

Teinemaa²,

Dumas

et al. 2021

2021 3rd International Conference on Process Mining (ICPM)

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Reducing cycle time is a recurrent concern in the field of business process management. Depending on the process, various interventions may be triggered to reduce the cycle time of a case, for example, using a faster shipping service in an order-to-delivery process or giving a phone call to a customer to obtain missing information rather than waiting passively. Each of these interventions comes with a cost. This paper tackles the problem of determining if and when to trigger a time-reducing intervention in a way that maximizes the total net gain. The paper proposes a prescriptive process monitoring method that uses orthogonal random forest models to estimate the causal effect of triggering a time-reducing intervention for each ongoing case of a process. Based on this causal effect estimate, the method triggers interventions according to a userdefined policy. The method is evaluated on two real-life logs.

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Section: Related Workmentioning

confidence: 99%

Prescriptive Process Monitoring for Cost-Aware Cycle Time Reduction

Bozorgi

Teinemaa²,

Dumas

et al. 2021

2021 3rd International Conference on Process Mining (ICPM)

Self Cite

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“…For example in [4,7], the goal is to find the structural equation model of the process features at the process level. Also in [3], cause-effect relations between a range of business process characteristics and process performance indicators are investigated using time series analysis. There is also existing work on finding the root of a problem at the case level.…”

Section: Related Workmentioning

confidence: 99%

Case Level Counterfactual Reasoning in Process Mining

Qafari¹,

Aalst²

2021

Preprint

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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 last 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. Our ProM plug-in produces recommendations that indicate how specific cases could have been handled differently to avoid a performance or compliance problem.

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“…For example, literature [24,25] proposed a method for automatically discovering process performance bottlenecks and deviations based on event data, but it did not explore causality. Literature [26] proposed a method based on time series analysis to detect the causal relationship between business process characteristics and process performance indicators. However, the Granger causality test method adopted did not consider the existence of latent variables; that is, it believed that the actual data satisfy the assumption of causality sufficiency.…”

Section: And the Triple Satisfiesmentioning

confidence: 99%

Causal Discovery of Flight Service Process Based on Event Sequence

Luo

Zhang

Xing

et al. 2021

Journal of Advanced Transportation

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The development of the civil aviation industry has continuously increased the requirements for the efficiency of airport ground support services. In the existing ground support research, there has not yet been a process model that directly obtains support from the ground support log to study the causal relationship between service nodes and flight delays. Most ground support studies mainly use machine learning methods to predict flight delays, and the flight support model they are based on is an ideal model. The study did not conduct an in-depth study of the causal mechanism behind the ground support link and did not reveal the true cause of flight delays. Therefore, there is a certain deviation in the prediction of flight delays by machine learning, and there is a certain deviation between the ideal model based on the research and the actual service process. Therefore, it is of practical significance to obtain the process model from the guarantee log and analyze its causality. However, the existing process causal factor discovery methods only do certain research when the assumption of causal sufficiency is established and does not consider the existence of latent variables. Therefore, this article proposes a framework to realize the discovery of process causal factors without assuming causal sufficiency. The optimized fuzzy mining process model is used as the service benchmark model, and the local causal discovery algorithm is used to discover the causal factors. Under this framework, this paper proposes a new Markov blanket discovery algorithm that does not assume causal sufficiency to discover causal factors and uses benchmark data sets for testing. Finally, the actual flight service data are used for causal discovery among flight service nodes. The local causal discovery algorithm proposed in this paper has a certain competitive advantage in accuracy, F1, and other aspects of the existing causal discovery algorithm. It avoids the occurrence of its dimensional disaster. Through the in-depth analysis of the flight safety reason node discovered by this method, it is found that the unreasonable scheduling of flight support personnel is an important reason for frequent flight delays at the airport.

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Discovering Causal Factors Explaining Business Process Performance Variation

Cited by 39 publications

References 18 publications

Prescriptive Process Monitoring for Cost-Aware Cycle Time Reduction

Prescriptive Process Monitoring for Cost-Aware Cycle Time Reduction

Case Level Counterfactual Reasoning in Process Mining

Causal Discovery of Flight Service Process Based on Event Sequence

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