From Observations to Causations: A GNN-Based Probabilistic Prediction Framework for Causal Discovery

Rezaur Rashid, Gabriel Terejanu

2025

Abstract

Causal discovery from observational data is challenging, especially with large datasets and complex relationships. Traditional methods often struggle with scalability and capturing global structural information. To overcome these limitations, we introduce a novel graph neural network (GNN)-based probabilistic framework that learns a probability distribution over the entire space of causal graphs, unlike methods that output a single deterministic graph. Our framework leverages a GNN that encodes both node and edge attributes into a unified graph representation, enabling the model to learn complex causal structures directly from data. The GNN model is trained on a diverse set of synthetic datasets augmented with statistical and information-theoretic measures, such as mutual information and conditional entropy, capturing both local and global data properties. We frame causal discovery as a supervised learning problem, directly predicting the entire graph structure. Our approach demonstrates superior performance, outperforming both traditional and recent non-GNN-based methods, as well as a GNN-based approach, in terms of accuracy and scalability on synthetic and real-world datasets without further training. This probabilistic framework significantly improves causal structure learning, with broad implications for decision-making and scientific discovery across various fields.

Paper Citation

in Harvard Style

Rashid R. and Terejanu G. (2025). From Observations to Causations: A GNN-Based Probabilistic Prediction Framework for Causal Discovery. In Proceedings of the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR; ISBN , SciTePress, pages 337-348. DOI: 10.5220/0013720400004000

in Bibtex Style

@conference{kdir25,
author={Rezaur Rashid and Gabriel Terejanu},
title={From Observations to Causations: A GNN-Based Probabilistic Prediction Framework for Causal Discovery},
booktitle={Proceedings of the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR},
year={2025},
pages={337-348},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013720400004000},
isbn={},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR
TI - From Observations to Causations: A GNN-Based Probabilistic Prediction Framework for Causal Discovery
SN -
AU - Rashid R.
AU - Terejanu G.
PY - 2025
SP - 337
EP - 348
DO - 10.5220/0013720400004000
PB - SciTePress