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. Retrieved from http://www.jmlr.org/papers/volume18/17-563/17-563.pdf
Published in
Journal of Machine Learning ResearchDate
2018Discipline
TilastotiedeCopyright
© the Authors, 2018.
Causal models communicate our assumptions about causes and effects in real-world phenomena.
Often the interest lies in the identification of the effect 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 inefficiency
of estimates when dealing with measurement error or missing data. We present graphical
criteria to detect variables which are redundant in identifying causal effects. We also provide
an improved version of a well-known identifiability algorithm that implements these criteria.
Publisher
MIT PressISSN Search the Publication Forum
1532-4435Keywords