Simplifying Probabilistic Expressions in Causal Inference
Tikka, S., & Karvanen, J. (2017). Simplifying Probabilistic Expressions in Causal Inference. Journal of Machine Learning Research, 18, 1-30. http://jmlr.csail.mit.edu/papers/volume18/16-166/16-166.pdf
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Journal of Machine Learning ResearchDate
2017Copyright
© 2017 Santtu Tikka and Juha Karvanen. This is an open access article distributed under the terms of a Creative Commons License.
Obtaining a non-parametric expression for an interventional distribution is one of the most
fundamental tasks in causal inference. Such an expression can be obtained for an identifiable
causal effect by an algorithm or by manual application of do-calculus. Often we are left
with a complicated expression which can lead to biased or inefficient estimates when missing
data or measurement errors are involved.
We present an automatic simplification algorithm that seeks to eliminate symbolically
unnecessary variables from these expressions by taking advantage of the structure of the
underlying graphical model. Our method is applicable to all causal effect formulas and is
readily available in the R package causaleffect.
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MIT PressISSN Search the Publication Forum
1532-4435Keywords
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http://jmlr.csail.mit.edu/papers/volume18/16-166/16-166.pdfPublication in research information system
https://converis.jyu.fi/converis/portal/detail/Publication/26991481
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Except where otherwise noted, this item's license is described as © 2017 Santtu Tikka and Juha Karvanen. This is an open access article distributed under the terms of a Creative Commons License.
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