Causal Effect Identification from Multiple Incomplete Data Sources R package dosearch version 1.0.12 CRAN Task View: CausalInference (https://CRAN.R-project.org/view=CausalInference) CRAN Task View: MissingData (https://CRAN.R-project.org/view=MissingData) 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) <http://ftp.cs.ucla.edu/pub/stat_ser/r443.pdf>, missing data (Mohan, Pearl, and Tian, 2013) <http://ftp.cs.ucla.edu/pub/stat_ser/r410.pdf>) 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>. Software Depends: R (>= 4.0) Imports: Rcpp LinkingTo: Rcpp Suggests: covr, dagitty, DiagrammeR, DOT, igraph, knitr, mockr, rmarkdown, testthat (>= 3.0.0) Santtu Tikka <santtuth@gmail.com> Comprehensive R Archive Network (CRAN) Santtu Tikka [aut, cre] (ORCID: <https://orcid.org/0000-0003-4039-4342>), Antti Hyttinen [ctb] (ORCID: <https://orcid.org/0000-0002-6649-3229>), Juha Karvanen [ctb] (ORCID: <https://orcid.org/0000-0001-5530-769X>) GPL (>= 3) 2025-09-26 application/tgz https://CRAN.R-project.org/package=dosearch doi:10.32614/CRAN.package.dosearch