Prescriptive Process Monitoring (PresPM) leverages machine learning to optimize business process outcomes by recommending real-time interventions. Two common approaches for recommending interventions in PresPM are Causal Inference (CI) and Reinforcement Learning (RL). Despite progress in these fields, significant gaps remain, including the lack of comprehensive comparisons of PresPM methods, the assumption of predefined intervention points and dimensions, limited handling of complex interventions (e.g., sequential interventions), and insuficient exploration of process interdependencies. This research proposes addressing these challenges by designing a simulator that generates synthetic data mimicking complex business processes, enabling the evaluation of approaches across diverse intervention types. Additionally, a novel causal decision point discovery algorithm will be developed, tailored for PresPM in business settings. The study also aims to enhance CI and RL techniques for safe and eficient handling of sequential interventions, decision timing, and process interdependencies. These contributions will be validated using both synthetic and real-world datasets, advancing the field of PresPM by providing novel methodologies for complex optimization problems.
AI-powered systems in Prescriptive Process Monitoring (PresPM) ofer transformative opportunities for businesses by enhancing decision-making. PresPM uses machine learning to analyze event log data from business processes, uncovering relationships between process variables and targets like delivery times, loan acceptance, patient recovery, product availability, or default rates. These systems try to optimize outcomes by prescribing real-time interventions, such as machine maintenance, customer calls, or loan application cancellations. Benefits include improved product and service quality, cost savings, and increased staf satisfaction.
After a thorough review of the literature, it is evident that two approaches for recommending
interventions are generally adopted: Causal Inference (CI) and Reinforcement Learning (RL). CI comprises
two primary subfields. The first, known as Causal Discovery (CD), focuses on identifying causal
relationships between variables. Its goal is to uncover the true causal structure of the data, typically
represented as a graph. An algorithm is said to solve the CD problem if it converges to the true structure
as the sample size increases. In Process Mining (PM), CD techniques are often used to pinpoint decision
points within a business process. The second subfield is concerned with estimating the efect of a
treatment, often denoted by the Individual Treatment Efect (ITE) in a particular data instance. Current
CI approaches in PresPM primarily concentrate on this second subfield, which can be divided into
two categories: direct and indirect CI. Direct CI entails training a machine learning model to directly
estimate the ITE of an intervention [
Despite the establishment of these initial approaches, many limitations in PresPM remain. Firstly,
there is a lack of extensive comparisons of PresPM methodologies. To the best of my knowledge,
no study has thoroughly and comprehensively compared multiple approaches. Additionally, existing
methods are typically focused on a narrow range of intervention types and limited complexity, often
neglecting more complex optimization problems. As described in [
To address the identified gaps in the research section, I propose the following research objectives:
Current PresPM research lacks comprehensive comparisons because it omits complex interventions
and a wide range of evaluation perspectives. To address this, I propose a synthetic data generator in
Python. Building on [
Although simulators in process mining have seen significant advancements in research [
Current prescriptive approaches often assume predetermined intervention points and dimensions,
lacking a systematic methodology for identification. To address this, I will explore and tailor CD methods
for PresPM in business settings. One research avenue is the exploration of various PM techniques, such
as methods that incorporate structural equation models as described in [
Current PresPM methodologies overlook complex intervention problems, such as sequential optimization and process interdependencies. I aim to adapt PresPM approaches to address these advanced Machine Learning tasks.
First, I aim to extend CI to handle intricate sequential tasks, considering challenges like decision
timing. This includes exploring optimal threshold optimization algorithms [
Additionally, I will adapt RL to these complex optimization challenges, e.g., intervention sequences
and process interdependencies, by improving its training eficiency. I will limit the RL action space to
decision points identified by my CD techniques and explore eficient sampling methods [ 18]. The aim is to
evaluate the suitability and efectiveness of these techniques for optimizing complex business processes.
Incorporating heuristics in RL will also be investigated to manage the increased search space. To ensure
real-world applicability of these adaptations, I will mitigate risks in online RL training by exploring
ofline RL techniques. These include using process discovery to construct training environments [
The novel methods will first be evaluated using the previously developed simulator that can generate all potential outcomes of a case, allowing for a thorough and accurate assessment. Afterward, the practical applicability of these methods will be validated using real-world datasets, ideally across various diverse business settings. [17] W. M. P. van der Aalst, Object-centric process mining: Unraveling the fabric of real processes,
Mathematics (2023). [18] V. Mai, K. Mani, L. Paull, Sample eficient deep reinforcement learning via uncertainty estimation, arXiv preprint arXiv:2201.01666 (2022). [19] S. Levine, A. Kumar, G. Tucker, J. Fu, Ofline reinforcement learning: Tutorial, review, and perspectives on open problems, arXiv (2020).