dosearch: Causal Effect Identification from Multiple Incomplete Data Sources

Identification of causal effects from arbitrary observational and experimental probability distributions via do-calculus and standard probability manipulations using a search-based algorithm. Allows for the presence of mechanisms related to selection bias (Bareinboim, E. and Tian, J. (2015) <>), transportability (Bareinboim, E. and Pearl, J. (2014) <>), missing data (Mohan, K. and Pearl, J. and Tian., J. (2013) <>) and arbitrary combinations of these. Also supports identification in the presence of context-specific independence (CSI) relations through labeled directed acyclic graphs (LDAG). For details on CSIs see Corander et al. (2019) <doi:10.1016/j.apal.2019.04.004>. For further information on the search-based approach see Tikka et al. (2019) <arXiv:1902.01073>.

Version: 1.0.4
Imports: Rcpp (≥ 0.12.19)
LinkingTo: Rcpp
Suggests: DOT
Published: 2019-10-22
Author: Santtu Tikka ORCID iD [aut, cre], Antti Hyttinen ORCID iD [ctb], Juha Karvanen ORCID iD [ctb]
Maintainer: Santtu Tikka <santtuth at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
SystemRequirements: C++11
Citation: dosearch citation info
Materials: NEWS
CRAN checks: dosearch results


Reference manual: dosearch.pdf
Package source: dosearch_1.0.4.tar.gz
Windows binaries: r-devel:, r-devel-gcc8:, r-release:, r-oldrel:
OS X binaries: r-release: dosearch_1.0.4.tgz, r-oldrel: dosearch_1.0.4.tgz
Old sources: dosearch archive


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