Causal Reasoning over Control-Flow Decisions in Process Models

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.