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) . 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) .


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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1.0.8 by Santtu Tikka, 5 months ago

Browse source code at

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

Documentation:   PDF Manual  

GPL (>= 2) license

Imports Rcpp

Suggests dagitty, DOT, igraph

Linking to Rcpp

System requirements: C++11

Imported by R6causal.

See at CRAN