Intervening With Confidence: Conformal Prescriptive Monitoring of Business Processes

• URL: http://arxiv.org/abs/2212.03710v1
• Date: Wed, 7 Dec 2022 15:29:21 GMT
• Title: Intervening With Confidence: Conformal Prescriptive Monitoring of Business Processes
• Authors: Mahmoud Shoush and Marlon Dumas
• Abstract summary: This paper proposes an approach to extend existing prescriptive process monitoring methods with 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.
• Score: 0.7487718119544158
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Probabilistic Conformal Prediction with Approximate Conditional Validity [81.30551968980143]
  We develop a new method for generating prediction sets that combines the flexibility of conformal methods with an estimate of the conditional distribution. Our method consistently outperforms existing approaches in terms of conditional coverage.
  arXiv  Detail & Related papers  (2024-07-01T20:44:48Z)
• Conformal Counterfactual Inference under Hidden Confounding [19.190396053530417]
  Predicting potential outcomes along with its uncertainty in a counterfactual world poses the foundamental challenge in causal inference. Existing methods that construct confidence intervals for counterfactuals either rely on the assumption of strong ignorability. We propose a novel approach based on transductive weighted conformal prediction, which provides confidence intervals for counterfactual outcomes with marginal converage guarantees.
  arXiv  Detail & Related papers  (2024-05-20T21:43:43Z)
• Prescriptive Process Monitoring Under Resource Constraints: A Reinforcement Learning Approach [0.3807314298073301]
  Reinforcement learning has been put forward as an approach to learning intervention policies through trial and error. Existing approaches in this space assume that the number of resources available to perform interventions in a process is unlimited. This paper argues that, in the presence of resource constraints, a key dilemma in the field of prescriptive process monitoring is to trigger interventions based not only on predictions of their necessity, timeliness, or effect but also on the uncertainty of these predictions and the level of resource utilization.
  arXiv  Detail & Related papers  (2023-07-13T05:31:40Z)
• Model Predictive Control with Gaussian-Process-Supported Dynamical Constraints for Autonomous Vehicles [82.65261980827594]
  We propose a model predictive control approach for autonomous vehicles that exploits learned Gaussian processes for predicting human driving behavior. A multi-mode predictive control approach considers the possible intentions of the human drivers.
  arXiv  Detail & Related papers  (2023-03-08T17:14:57Z)
• When to intervene? Prescriptive Process Monitoring Under Uncertainty and Resource Constraints [0.7487718119544158]
  Prescriptive process monitoring approaches leverage historical data to prescribe runtime interventions that will likely prevent negative case outcomes or improve a process's performance. Previous proposals in this field rely on intervention policies that consider only the current state of a given case. This paper addresses these gaps by introducing a prescriptive process monitoring method that filters and ranks ongoing cases based on prediction scores, prediction uncertainty, and causal effect of the intervention, and triggers interventions to maximize a gain function.
  arXiv  Detail & Related papers  (2022-06-15T18:18:33Z)
• Prescriptive Process Monitoring: Quo Vadis? [64.39761523935613]
  The paper studies existing methods in this field via a Systematic Literature Review ( SLR) The SLR provides insights into challenges and areas for future research that could enhance the usefulness and applicability of prescriptive process monitoring methods.
  arXiv  Detail & Related papers  (2021-12-03T08:06:24Z)
• Prescriptive Process Monitoring Under Resource Constraints: A Causal Inference Approach [0.9645196221785693]
  Existing prescriptive process monitoring techniques assume that the number of interventions that may be triggered is unbounded. This paper proposes a prescriptive process monitoring technique that triggers interventions to optimize a cost function under fixed resource constraints.
  arXiv  Detail & Related papers  (2021-09-07T06:42:33Z)
• Reliable Off-policy Evaluation for Reinforcement Learning [53.486680020852724]
  In a sequential decision-making problem, off-policy evaluation estimates the expected cumulative reward of a target policy. We propose a novel framework that provides robust and optimistic cumulative reward estimates using one or multiple logged data.
  arXiv  Detail & Related papers  (2020-11-08T23:16:19Z)
• CoinDICE: Off-Policy Confidence Interval Estimation [107.86876722777535]
  We study high-confidence behavior-agnostic off-policy evaluation in reinforcement learning. We show in a variety of benchmarks that the confidence interval estimates are tighter and more accurate than existing methods.
  arXiv  Detail & Related papers  (2020-10-22T12:39:11Z)
• Counterfactual Predictions under Runtime Confounding [74.90756694584839]
  We study the counterfactual prediction task in the setting where all relevant factors are captured in the historical data. We propose a doubly-robust procedure for learning counterfactual prediction models in this setting.
  arXiv  Detail & Related papers  (2020-06-30T15:49:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.