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>.
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 [aut, cre], Antti Hyttinen [ctb], Juha Karvanen [ctb] Maintainer: Santtu Tikka <santtuth at gmail.com> BugReports: https://github.com/santikka/dosearch/issues License: GPL (≥ 3) URL: https://github.com/santikka/dosearch NeedsCompilation: yes Citation: dosearch citation info Materials: NEWS In views: CausalInference, MissingData CRAN checks: dosearch results Documentation: Downloads: Reverse dependencies: Linking:Please use the canonical form https://CRAN.R-project.org/package=dosearch to link to this page.
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