Package: dosearch 1.0.11

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) <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>.

Authors:Santtu Tikka [aut, cre], Antti Hyttinen [ctb], Juha Karvanen [ctb]

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Bug tracker:https://github.com/santikka/dosearch/issues

On CRAN:

c-plus-pluscausal-inferencecausal-modelscausalitycausality-algorithmsdirected-acyclic-graphgraphslabeled-graphs

5 exports 7 stars 1.67 score 1 dependencies 1 dependents 7 scripts 594 downloads

Last updated 2 months agofrom:bfebae7159. Checks:OK: 9. Indexed: yes.

Exports:dosearchget_benchmarkget_derivationget_formulais_identifiable

Dependencies:Rcpp