Causal Effect Identification from Multiple Incomplete Data
Sources
Description
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) .