Title

Causal discovery from nonstationary/heterogeneous data : skeleton estimation and orientation determination

Document Type

Book chapter

Source Publication

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence

Publication Date

2017

First Page

1347

Last Page

1353

Publisher

International Joint Conferences on Artificial Intelligence

Keywords

Knowledge Representation, Reasoning, and Logic: Action, Change and Causality Uncertainty in AI: Uncertainty in AI

Abstract

It is commonplace to encounter nonstationary or heterogeneous data, of which the underlying generating process changes over time or across data sets (the data sets may have different experimental conditions or data collection conditions). Such a distribution shift feature presents both challenges and opportunities for causal discovery. In this paper we develop a principled framework for causal discovery from such data, called Constraint-based causal Discovery from Nonstationary/heterogeneous Data (CD-NOD), which addresses two important questions. First, we propose an enhanced constraint-based procedure to detect variables whose local mechanisms change and recover the skeleton of the causal structure over observed variables. Second, we present a way to determine causal orientations by making use of independence changes in the data distribution implied by the underlying causal model, benefiting from information carried by changing distributions. Experimental results on various synthetic and real-world data sets are presented to demonstrate the efficacy of our methods.

DOI

10.24963/ijcai.2017/187

Funding Information

Research conducted in this paper was supported by the National Institutes of Health (NIH) under Award Numbers NIH–1R01EB022858-01 FAIN–R01EB022858, NIH–1R01LM012087, and NIH–5U54HG008540-02 FAIN–U54HG008540.

Publisher Statement

Copyright © 2017 International Joint Conferences on Artificial Intelligence. Access to external full text or publisher's version may require subscription.

Additional Information

ISBN of the source publication: 9780999241103

Full-text Version

Publisher’s Version

Recommended Citation

Zhang, K., Huang, B., Zhang, J., Glymour, C. & Schölkopf, B. (2017). Causal discovery from nonstationary/heterogeneous data: skeleton estimation and orientation determination. In C. Sierra (Ed.), Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (pp. 1347-1353). Australia: International Joint Conferences on Artificial Intelligence. doi: 10.24963/ijcai.2017/187