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 et al. (2021) <doi:10.18637/jss.v099.i05>. Allows for the presence of mechanisms related to selection bias (Bareinboim, E. and Tian, J. (2015) <>), transportability (Bareinboim, E. and Pearl, J. (2014) <>), missing data (Mohan, K. and Pearl, J. and Tian., J. (2013) <>) 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>.

Package details

AuthorSanttu Tikka [aut, cre] (<>), Antti Hyttinen [ctb] (<>), Juha Karvanen [ctb] (<>)
MaintainerSanttu Tikka <>
LicenseGPL (>= 2)
Package repositoryView on CRAN
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dosearch documentation built on Aug. 19, 2021, 5:07 p.m.