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 by Tikka, Hyttinen and Karvanen (2021) <doi:10.18637/jss.v099.i05>. Allows for the presence of mechanisms related to selection bias (Bareinboim and Tian, 2015) <doi:10.1609/aaai.v29i1.9679>, transportability (Bareinboim and Pearl, 2014) <>, missing data (Mohan, Pearl, and Tian, 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>.

Version: 1.0.11
Depends: R (≥ 4.0)
Imports: Rcpp
LinkingTo: Rcpp
Suggests: covr, dagitty, DiagrammeR, DOT, igraph, knitr, mockr, rmarkdown, testthat (≥ 3.0.0)
Published: 2024-07-16
DOI: 10.32614/CRAN.package.dosearch
Author: Santtu Tikka ORCID iD [aut, cre], Antti Hyttinen ORCID iD [ctb], Juha Karvanen ORCID iD [ctb]
Maintainer: Santtu Tikka <santtuth at>
License: GPL (≥ 3)
NeedsCompilation: yes
Citation: dosearch citation info
Materials: NEWS
In views: CausalInference, MissingData
CRAN checks: dosearch results


Reference manual: dosearch.pdf
Vignettes: Identifying Causal Effects using dosearch


Package source: dosearch_1.0.11.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): dosearch_1.0.11.tgz, r-oldrel (arm64): dosearch_1.0.11.tgz, r-release (x86_64): dosearch_1.0.11.tgz, r-oldrel (x86_64): dosearch_1.0.11.tgz
Old sources: dosearch archive

Reverse dependencies:

Reverse imports: R6causal


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