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Enhancing Identification of Causal Effects by Pruning

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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
Published in
Journal of Machine Learning Research
Authors
Tikka, Santtu |
Karvanen, Juha
Date
2018
Discipline
TilastotiedeStatistics
Copyright
© the Authors, 2018.

 
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.
Publisher
MIT Press
ISSN Search the Publication Forum
1532-4435
Keywords
causal inference identiafiability causal model algorithm päättely tunnistaminen algoritmit leikkaus (kasvit) koneoppiminen kausaliteetti

Original source
http://www.jmlr.org/papers/volume18/17-563/17-563.pdf

URI

http://urn.fi/URN:NBN:fi:jyu-201807043469

Publication in research information system

https://converis.jyu.fi/converis/portal/detail/Publication/28149935

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  • Matemaattis-luonnontieteellinen tiedekunta [4971]
Related funder(s)
Academy of Finland
Funding program(s)
Research profiles, AoF
Additional information about funding
We wish to thank Professor Jukka Nyblom for his comments that greatly helped to improve this paper. We also thank the anonymous reviewers for their insightful feedback. The work belongs to the profiling area ”Decision analytics utilizing causal models and multiobjective optimization” (DEMO) supported by Academy of Finland (grant number 311877).

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