# Intervening With Confidence: Conformal Prescriptive Monitoring of Business Processes

arXiv: https://arxiv.org/abs/2212.03710 · HTML: https://mlsanity.com/paper/2212.03710/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 20 references that are in the map.
- First posted on arXiv 7 Dec 2022.

## Builds on (most cited first, 10 of 10)

- [Conformal Prediction Under Covariate Shift](https://mlsanity.com/paper/1904.06019/) (2019)
- [Conformal Prediction: a Unified Review of Theory and New Challenges](https://mlsanity.com/paper/2005.07972/) (2020)
- [Prescriptive Business Process Monitoring for Recommending Next Best Actions](https://mlsanity.com/paper/2008.08693/) (2020)
- [Fire Now, Fire Later: Alarm-Based Systems for Prescriptive Process Monitoring](https://mlsanity.com/paper/1905.09568/) (2019)
- [Prescriptive Process Monitoring for Cost-Aware Cycle Time Reduction](https://mlsanity.com/paper/2105.07111/) (2021)
- [Alarm-Based Prescriptive Process Monitoring](https://mlsanity.com/paper/1803.08706/) (2018)
- [Prescriptive Process Monitoring: Quo Vadis?](https://mlsanity.com/paper/2112.01769/) (2021)
- [Prescriptive Process Monitoring Under Resource Constraints: A Causal Inference Approach](https://mlsanity.com/paper/2109.02894/) (2021)
- [When to intervene? Prescriptive Process Monitoring Under Uncertainty and Resource Constraints](https://mlsanity.com/paper/2206.07745/) (2022)
- [Goal-Oriented Next Best Activity Recommendation using Reinforcement Learning](https://mlsanity.com/paper/2205.03219/) (2022)

## Built on by (most cited first, 5 of 5)

- [The WHY in Business Processes: Discovery of Causal Execution Dependencies](https://mlsanity.com/paper/2310.14975/) (2023)
- [Automatically Reconciling the Trade-off between Prediction Accuracy and Earliness in Prescriptive Business Process Monitoring](https://mlsanity.com/paper/2307.05939/) (2023)
- [Prescriptive Process Monitoring Under Resource Constraints: A Reinforcement Learning Approach](https://mlsanity.com/paper/2307.06564/) (2023)
- [How Will My Business Process Unfold? Predicting Case Suffixes With Start and End Timestamps](https://mlsanity.com/paper/2509.14536/) (2025)
- [SCOPE: Sequential Causal Optimization of Process Interventions](https://mlsanity.com/paper/2512.17629/) (2025)

## Abstract (arXiv)

Prescriptive process monitoring methods seek to improve the performance of a process by selectively triggering interventions at runtime (e.g., offering a discount to a customer) to increase the probability of a desired case outcome (e.g., a customer making a purchase). The backbone of a prescriptive process monitoring method is an intervention policy, which determines for which cases and when an intervention should be executed. Existing methods in this field rely on predictive models to define intervention policies; specifically, they consider policies that trigger an intervention when the estimated probability of a negative outcome exceeds a threshold. However, the probabilities computed by a predictive model may come with a high level of uncertainty (low confidence), leading to unnecessary interventions and, thus, wasted effort. This waste is particularly problematic when the resources available to execute interventions are limited. To tackle this shortcoming, this paper proposes an approach to extend existing prescriptive process monitoring methods with so-called conformal predictions, i.e., predictions with confidence guarantees. An empirical evaluation using real-life public datasets shows that conformal predictions enhance the net gain of prescriptive process monitoring methods under limited resources.