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Identifying Causal Effects with the R Package causaleffect

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  • Tikka, Santtu
  • Karvanen, Juha

Abstract

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.

Suggested Citation

  • Tikka, Santtu & Karvanen, Juha, 2017. "Identifying Causal Effects with the R Package causaleffect," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 76(i12).
    • Handle: RePEc:jss:jstsof:v:076:i12
    DOI: http://hdl.handle.net/10.18637/jss.v076.i12
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    Cited by:

    1. Raputsoane, Leroi, 2018. "Leader followership in monetary policy coordination," MPRA Paper 121903, University Library of Munich, Germany.
    2. Raputsoane, Leroi, 2018. "Monetary policy coordination leader followership," MPRA Paper 85684, University Library of Munich, Germany.
    3. Aurora C. Schmidt & Christopher J. Cameron & Corey Lowman & Joshua Brulé & Amruta J. Deshpande & Seyyed A. Fatemi & Vladimir Barash & Ariel M. Greenberg & Cash J. Costello & Eli S. Sherman & Rohit Bha, 2023. "Searching for explanations: testing social scientific methods in synthetic ground-truthed worlds," Computational and Mathematical Organization Theory, Springer, vol. 29(1), pages 156-187, March.
    4. Christopher Hagedorn & Johannes Huegle & Rainer Schlosser, 2022. "Understanding unforeseen production downtimes in manufacturing processes using log data-driven causal reasoning," Journal of Intelligent Manufacturing, Springer, vol. 33(7), pages 2027-2043, October.
    5. Brathwaite, Timothy & Walker, Joan L., 2018. "Causal inference in travel demand modeling (and the lack thereof)," Journal of choice modelling, Elsevier, vol. 26(C), pages 1-18.
    6. Lauri Valkonen & Jouni Helske & Juha Karvanen, 2023. "Estimating the causal effect of timing on the reach of social media posts," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(2), pages 493-507, June.
    7. Jouni Helske & Santtu Tikka & Juha Karvanen, 2021. "Estimation of causal effects with small data in the presence of trapdoor variables," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(3), pages 1030-1051, July.

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