Identifying Causal Effects with the R Package causaleffect
Tikka, S., & Karvanen, J. (2017). Identifying Causal Effects with the R Package causaleffect. Journal of Statistical Software, 76 (12), 1-30. doi:10.18637/jss.v076.i12
Published inJournal of Statistical Software
© the Authors, 2017. This is an open access article distributed under a Creative Commons license.
Do-calculus is concerned with estimating the interventional distribution of an action from the observed joint probability distribution of the variables in a given causal structure. All identifiable causal effects can be derived using the rules of do-calculus, but the rules themselves do not give any direct indication whether the effect in question is identifiable or not. Shpitser and Pearl (2006b) constructed an algorithm for identifying joint interventional distributions in causal models, which contain unobserved variables and induce directed acyclic graphs. This algorithm can be seen as a repeated application of the rules of do-calculus and known properties of probabilities, and it ultimately either derives an expression for the causal distribution, or fails to identify the effect, in which case the effect is non-identifiable. In this paper, the R package causaleffect is presented, which provides an implementation of this algorithm. Functionality of causaleffect is also demonstrated through examples. ...
PublisherFoundation for Open Access Statistics
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Except where otherwise noted, this item's license is described as © the Authors, 2017. This is an open access article distributed under a Creative Commons license.
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