Causal Effect Identification from Multiple Incomplete Data
Sources
c(
person(given = "Santtu",
family = "Tikka",
role = c("aut", "cre"),
email = "santtuth@gmail.com",
comment = c(ORCID = "0000-0003-4039-4342")),
person(given = "Antti",
family = "Hyttinen",
role = "ctb",
comment = c(ORCID = "0000-0002-6649-3229")),
person(given = "Juha",
family = "Karvanen",
role = "ctb",
comment = c(ORCID = "0000-0001-5530-769X")))
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.

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>.

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) <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>.