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) <http://ftp.cs.ucla.edu/pub/stat_ser/r445.pdf>), transportability (Bareinboim, E. and Pearl, J. (2014) <http://ftp.cs.ucla.edu/pub/stat_ser/r443.pdf>), missing data (Mohan, K. and Pearl, J. and Tian., J. (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>. For further information on the search-based approach see Tikka et al. (2019) <arXiv:1902.01073>.
|Author||Santtu Tikka [aut, cre] (<https://orcid.org/0000-0003-4039-4342>), Antti Hyttinen [ctb] (<https://orcid.org/0000-0002-6649-3229>), Juha Karvanen [ctb] (<https://orcid.org/0000-0001-5530-769X>)|
|Maintainer||Santtu Tikka <firstname.lastname@example.org>|
|License||GPL (>= 2)|
|Package repository||View on CRAN|
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