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santikka
dosearch: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, Hyttinen and Karvanen (2021) <doi:10.18637/jss.v099.i05>. Allows for the presence of mechanisms related to selection bias (Bareinboim and Tian, 2015) <doi:10.1609/aaai.v29i1.9679>, transportability (Bareinboim and Pearl, 2014) <http://ftp.cs.ucla.edu/pub/stat_ser/r443.pdf>, missing data (Mohan, Pearl, and Tian, 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>.
Maintained by Santtu Tikka. Last updated 8 months ago.
c-plus-pluscausal-inferencecausal-modelscausalitycausality-algorithmsdirected-acyclic-graphgraphslabeled-graphscpp
51.5 match 7 stars 5.32 score 8 scripts 1 dependentssantikka
causaleffect:Deriving Expressions of Joint Interventional Distributions and Transport Formulas in Causal Models
Functions for identification and transportation of causal effects. Provides a conditional causal effect identification algorithm (IDC) by Shpitser, I. and Pearl, J. (2006) <http://ftp.cs.ucla.edu/pub/stat_ser/r329-uai.pdf>, an algorithm for transportability from multiple domains with limited experiments by Bareinboim, E. and Pearl, J. (2014) <http://ftp.cs.ucla.edu/pub/stat_ser/r443.pdf>, and a selection bias recovery algorithm by Bareinboim, E. and Tian, J. (2015) <http://ftp.cs.ucla.edu/pub/stat_ser/r445.pdf>. All of the previously mentioned algorithms are based on a causal effect identification algorithm by Tian , J. (2002) <http://ftp.cs.ucla.edu/pub/stat_ser/r309.pdf>.
Maintained by Santtu Tikka. Last updated 2 years ago.
causal-inferencecausal-modelscausality-algorithmsdirected-acyclic-graphgraphsidentifiabilityidentificationigraph
50.9 match 29 stars 5.28 score 44 scripts 1 dependentssantikka
cfid:Identification of Counterfactual Queries in Causal Models
Facilitates the identification of counterfactual queries in structural causal models via the ID* and IDC* algorithms by Shpitser, I. and Pearl, J. (2007, 2008) <arXiv:1206.5294>, <https://jmlr.org/papers/v9/shpitser08a.html>. Provides a simple interface for defining causal diagrams and counterfactual conjunctions. Construction of parallel worlds graphs and counterfactual graphs is carried out automatically based on the counterfactual query and the causal diagram. See Tikka, S. (2023) <doi:10.32614/RJ-2023-053> for a tutorial of the package.
Maintained by Santtu Tikka. Last updated 8 months ago.
causal-inferencecausal-modelscausality-algorithmscounterfactualcounterfactualsdirected-acyclic-graphidentifiability
36.4 match 7 stars 4.02 score 2 scripts 1 dependents