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Learning High-dimensional Directed Acyclic Graphs with Mixed Data-types.

Bryan Andrews¹, Joseph Ramsey², Gregory F Cooper³

¹Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA 15260, USA.

Proceedings of Machine Learning Research

|August 28, 2019

Summary

This summary is machine-generated.

This study introduces a new method for causal structure learning in complex, high-dimensional datasets with mixed data types. The degenerate Gaussian score efficiently handles continuous and discrete variables, improving causal discovery.

Keywords:

Causal Structure Learning Directed Acyclic Graphs High-dimensional Data Mixed Data-types

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Area of Science:

Causal inference
Machine learning
Statistical modeling

Background:

Causal structure learning has advanced in high-dimensional and mixed data-type settings.
The intersection of high-dimensional and mixed data-types remains understudied due to modeling complexities.

Purpose of the Study:

To efficiently extend causal structure learning algorithms to high-dimensional data with mixed data types.
To address the understudied problem of causal discovery in complex datasets.

Main Methods:

Characterization of a statistical model for continuous and discrete variables.
Derivation of a degenerate Gaussian (DG) score for mixed data-types.
Analysis of the asymptotic properties of the DG score.

Main Results:

A novel DG score was developed for mixed data-types.
The DG score demonstrates practical utility in learning causal structures.
Asymptotic properties of the DG score were theoretically analyzed.

Conclusions:

The proposed DG score offers an efficient approach for causal structure learning in high-dimensional, mixed data settings.
This work provides a foundation for future research in complex causal discovery.