A1 Journal article (refereed)
Enhancing Identification of Causal Effects by Pruning (2018)

Tikka, S., & Karvanen, J. (2018). Enhancing Identification of Causal Effects by Pruning. Journal of Machine Learning Research, 18, 1-23. http://www.jmlr.org/papers/volume18/17-563/17-563.pdf

JYU authors or editors

Publication details

All authors or editors: Tikka, Santtu; Karvanen, Juha

Journal or series: Journal of Machine Learning Research

ISSN: 1532-4435

eISSN: 1533-7928

Publication year: 2018

Volume: 18

Issue number: 0

Pages range: 1-23

Publisher: MIT Press

Publication country: United States

Publication language: English

Persistent website address: http://www.jmlr.org/papers/volume18/17-563/17-563.pdf

Publication open access: Openly available

Publication channel open access: Open Access channel

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/58881


Causal models communicate our assumptions about causes and e ects in real-world phenomena. Often the interest lies in the identification of the e ect of an action which means deriving an expression from the observed probability distribution for the interventional distribution resulting from the action. In many cases an identifiability algorithm may return a complicated expression that contains variables that are in fact unnecessary. In practice this can lead to additional computational burden and increased bias or ine ciency of estimates when dealing with measurement error or missing data. We present graphical criteria to detect variables which are redundant in identifying causal e ects. We also provide an improved version of a well-known identifiability algorithm that implements these criteria.

Keywords: machine learning; inference; causality; recognition; algorithms; pruning

Free keywords: causal inference; identiafiability; causal model; algorithm

Contributing organizations

Related projects

Ministry reporting: Yes

Reporting Year: 2018

JUFO rating: 3

Last updated on 2021-02-08 at 10:25