Enhancing Identification of Causal Effects by Pruning
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 inJournal of Machine Learning Research
© 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.
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Related funder(s)Academy of Finland
Funding program(s)Research profiles, AoF
Additional information about fundingWe 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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