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. 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>.

Version:	1.0.4
Imports:	Rcpp (≥ 0.12.19)
LinkingTo:	Rcpp
Suggests:	DOT
Published:	2019-10-22
Author:	Santtu Tikka [aut, cre], Antti Hyttinen [ctb], Juha Karvanen [ctb]
Maintainer:	Santtu Tikka <santtuth at gmail.com>
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation:	yes
SystemRequirements:	C++11
Citation:	dosearch citation info
Materials:	NEWS
CRAN checks:	dosearch results

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