---
_id: '60680'
abstract:
- lang: eng
  text: "Classical machine learning techniques often struggle with overfitting and
    unreliable predictions when exposed to novel conditions. Introducing causality
    into the modelling process offers a promising way to mitigate these challenges
    by enhancing predictive robustness. However, constructing an initial causal graph
    manually using domain knowledge is time-consuming, particularly in complex time
    series with numerous variables. To address this, causal discovery algorithms can
    provide a preliminary causal structure that domain experts can refine. This study
    investigates causal feature selection with domain knowledge using a data center
    system as an example. We use simulated time-series data to compare \r\ndifferent
    causal feature selection with traditional machine-learning feature selection methods.
    Our results show that predictions based on causal features are more robust compared
    to those derived from traditional methods. These findings underscore the potential
    of combining causal discovery algorithms with human expertise to improve machine
    learning applications."
author:
- first_name: David Ricardo
  full_name: Zapata Gonzalez, David Ricardo
  id: '105506'
  last_name: Zapata Gonzalez
- first_name: Marcel
  full_name: Meyer, Marcel
  id: '105120'
  last_name: Meyer
- first_name: Oliver
  full_name: Müller, Oliver
  id: '72849'
  last_name: Müller
citation:
  ama: 'Zapata Gonzalez DR, Meyer M, Müller O. Bridging the gap between data-driven
    and theory-driven modelling – leveraging causal machine learning for integrative
    modelling of dynamical systems. In: ; 2025.'
  apa: Zapata Gonzalez, D. R., Meyer, M., &#38; Müller, O. (2025). <i>Bridging the
    gap between data-driven and theory-driven modelling – leveraging causal machine
    learning for integrative modelling of dynamical systems</i>. European Conference
    on Information Systems, Amman, Jordan.
  bibtex: '@inproceedings{Zapata Gonzalez_Meyer_Müller_2025, title={Bridging the gap
    between data-driven and theory-driven modelling – leveraging causal machine learning
    for integrative modelling of dynamical systems}, author={Zapata Gonzalez, David
    Ricardo and Meyer, Marcel and Müller, Oliver}, year={2025} }'
  chicago: Zapata Gonzalez, David Ricardo, Marcel Meyer, and Oliver Müller. “Bridging
    the Gap between Data-Driven and Theory-Driven Modelling – Leveraging Causal Machine
    Learning for Integrative Modelling of Dynamical Systems,” 2025.
  ieee: D. R. Zapata Gonzalez, M. Meyer, and O. Müller, “Bridging the gap between
    data-driven and theory-driven modelling – leveraging causal machine learning for
    integrative modelling of dynamical systems,” presented at the European Conference
    on Information Systems, Amman, Jordan, 2025.
  mla: Zapata Gonzalez, David Ricardo, et al. <i>Bridging the Gap between Data-Driven
    and Theory-Driven Modelling – Leveraging Causal Machine Learning for Integrative
    Modelling of Dynamical Systems</i>. 2025.
  short: 'D.R. Zapata Gonzalez, M. Meyer, O. Müller, in: 2025.'
conference:
  end_date: 18.06.2025
  location: Amman, Jordan
  name: European Conference on Information Systems
  start_date: 16.06.2025
date_created: 2025-07-21T07:52:03Z
date_updated: 2025-07-22T06:30:37Z
department:
- _id: '196'
keyword:
- Causal Machine Learning
- Causality in Time Series
- Causal Discovery
- Human-Machine  Collaboration
language:
- iso: eng
main_file_link:
- url: https://aisel.aisnet.org/ecis2025/bus_analytics/bus_analytics/2/
status: public
title: Bridging the gap between data-driven and theory-driven modelling – leveraging
  causal machine learning for integrative modelling of dynamical systems
type: conference
user_id: '72849'
year: '2025'
...