Proceedings

An Information-Theoretic Approach on Causal Structure Learning for Heterogeneous Data Characteristics of Real-World Scenarios

Johannes Huegle

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Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. Doctoral Consortium. Pages 4891-4892.

https://doi.org/10.24963/ijcai.2021/677

Keywords

  • Computer Vision: Statistical Methods and Machine Learning
  • Machine Learning: Learning Graphical Models
  • Machine Learning: Probabilistic Machine Learning
  • Knowledge Representation and Reasoning: Action, Change and Causality

Abstract

While the knowledge about the structures of a system’s underlying causal relationships is crucial within many real-world scenarios, the omnipresence of heterogeneous data characteristics impedes applying methods for causal structure learning (CSL).

In this dissertation project, we reduce the barriers for the transfer of CSL into practice with threefold contributions:

(1) We derive an information-theoretic conditional independence test that, incorporated into methods for CSL, improves the accuracy for non-linear and mixed discrete-continuous causal relationships;

(2) We develop a modular pipeline that covers the essential components required for a comprehensive benchmarking to support the transferability into practice;

(3) We evaluate opportunities and challenges of CSL within different real-world scenarios from genetics and discrete manufacturing to demonstrate the accuracy of our approach in practice.

Johannes Huegle. 2021. An Information-Theoretic Approach on Causal Structure Learning for Heterogeneous Data Characteristics of Real-World Scenarios. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21. IJCAI, 4891–4892. https://doi.org/10.24963/ijcai.2021/677
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