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) . For further information on the search-based approach see Tikka et al. (2019) .


News

Changes from version 1.0.1 to 1.0.2

  • Added an example on how to produce an image from the DOT derivation.
  • Added a warning when the response indicator for a proxy variable is not present in any data source.

Changes from version 1.0.0 to 1.0.1

  • Added a Vignette describing the search procedure.

Reference manual

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install.packages("dosearch")

1.0.3 by Santtu Tikka, 3 days ago


Browse source code at https://github.com/cran/dosearch


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


Documentation:   PDF Manual  


GPL (>= 2) license


Imports Rcpp

Suggests DOT

Linking to Rcpp

System requirements: C++11


See at CRAN