Inferring Causal Direction from Relational Data

David Arbour, Katerina Marazopoulou, David Jensen
Proceedings of the 32nd Conference on Uncertainty in Artificial Intelligence, PMLR R14:562-571, 2016.

Abstract

Inferring the direction of causal dependence from observational data is a fundamental problem in many scientific fields. Significant progress has been made in inferring causal direction from data that are independent and identically distributed (i.i.d.), but little is understood about this problem in the more general relational setting. This work examines the task of inferring the causal direction of peer dependence in relational data. We show that, in contrast to the i.i.d. setting, the direction of peer dependence can be inferred using simple procedures, regardless of the form of the underlying distribution, and we provide a theoretical characterization on the identifiability of direction. We then examine the conditions under which the presence of confounding can be detected. Finally, we demonstrate the efficacy of the proposed methods with synthetic experiments, and we provide an application on real-world data.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR14-arbour16a, title = {Inferring Causal Direction from Relational Data}, author = {Arbour, David and Marazopoulou, Katerina and Jensen, David}, booktitle = {Proceedings of the 32nd Conference on Uncertainty in Artificial Intelligence}, pages = {562--571}, year = {2016}, editor = {Ihler, Alexander and Janzing, Dominik}, volume = {R14}, series = {Proceedings of Machine Learning Research}, month = {25--29 Jun}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r14/main/assets/arbour16a/arbour16a.pdf}, url = {https://proceedings.mlr.press/r14/arbour16a.html}, abstract = {Inferring the direction of causal dependence from observational data is a fundamental problem in many scientific fields. Significant progress has been made in inferring causal direction from data that are independent and identically distributed (i.i.d.), but little is understood about this problem in the more general relational setting. This work examines the task of inferring the causal direction of peer dependence in relational data. We show that, in contrast to the i.i.d. setting, the direction of peer dependence can be inferred using simple procedures, regardless of the form of the underlying distribution, and we provide a theoretical characterization on the identifiability of direction. We then examine the conditions under which the presence of confounding can be detected. Finally, we demonstrate the efficacy of the proposed methods with synthetic experiments, and we provide an application on real-world data.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Inferring Causal Direction from Relational Data %A David Arbour %A Katerina Marazopoulou %A David Jensen %B Proceedings of the 32nd Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2016 %E Alexander Ihler %E Dominik Janzing %F pmlr-vR14-arbour16a %I PMLR %P 562--571 %U https://proceedings.mlr.press/r14/arbour16a.html %V R14 %X Inferring the direction of causal dependence from observational data is a fundamental problem in many scientific fields. Significant progress has been made in inferring causal direction from data that are independent and identically distributed (i.i.d.), but little is understood about this problem in the more general relational setting. This work examines the task of inferring the causal direction of peer dependence in relational data. We show that, in contrast to the i.i.d. setting, the direction of peer dependence can be inferred using simple procedures, regardless of the form of the underlying distribution, and we provide a theoretical characterization on the identifiability of direction. We then examine the conditions under which the presence of confounding can be detected. Finally, we demonstrate the efficacy of the proposed methods with synthetic experiments, and we provide an application on real-world data. %Z Reissued by PMLR on 04 October 2026.
APA
Arbour, D., Marazopoulou, K. & Jensen, D.. (2016). Inferring Causal Direction from Relational Data. Proceedings of the 32nd Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R14:562-571 Available from https://proceedings.mlr.press/r14/arbour16a.html. Reissued by PMLR on 04 October 2026.

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