Causal Subgroup Analysis for Detecting Confounding

Authors:
Martin Atzmueller;Frank Puppe
Affiliations:
Department of Computer Science VI, University of Würzburg, Würzburg, Germany 97074;Department of Computer Science VI, University of Würzburg, Würzburg, Germany 97074
Venue:
Applications of Declarative Programming and Knowledge Management
Year:
2009

Citing 7
Cited 0

A Simple Constraint-Based Algorithm for Efficiently Mining Observational Databases for Causal Relationships

Data Mining and Knowledge Discovery
Scalable Techniques for Mining Causal Structures

Data Mining and Knowledge Discovery
An Algorithm for Multi-relational Discovery of Subgroups

PKDD '97 Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery
Subgroup Discovery with CN2-SD

The Journal of Machine Learning Research
Onto Confounding-Aware Subgroup Discovery

ICTAI '07 Proceedings of the 19th IEEE International Conference on Tools with Artificial Intelligence - Volume 02
Exploiting background knowledge for knowledge-intensive subgroup discovery

IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
SD-map: a fast algorithm for exhaustive subgroup discovery

PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a causal subgroup analysis approach for the detection of confounding: We show how to identify (causal) relations between subgroups by generating an extended causal subgroup network utilizing background knowledge. Using the links within the network we can identify relations that are potentially confounded by external (confounding) factors. In a semi-automatic approach, the network and the discovered relations are then presented to the user as an intuitive visualization. The applicability and benefit of the presented technique is illustrated by examples from a case-study in the medical domain.