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)
Changes from version 1.0.1 to 1.0.2
Changes from version 1.0.0 to 1.0.1