Causal Effect Identification from Multiple Incomplete Data
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) <10.18637>. 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) <10.1016>.10.1016>10.18637>
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.