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codemeta.json
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{
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"@type": "SoftwareSourceCode",
"identifier": "dosearch",
"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) <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>.",
"name": "dosearch: Causal Effect Identification from Multiple Incomplete Data Sources",
"codeRepository": "https://github.com/santikka/dosearch",
"issueTracker": "https://github.com/santikka/dosearch/issues",
"license": "https://spdx.org/licenses/GPL-3.0",
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"givenName": "Santtu",
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"name": "Causal Effect Identification from Multiple Incomplete Data Sources: A General Search-Based Approach",
"identifier": "10.18637/jss.v099.i05",
"pagination": "1--40",
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"releaseNotes": "https://github.com/santikka/dosearch/blob/master/NEWS",
"readme": "https://github.com/santikka/dosearch/blob/master/README.md",
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"keywords": ["causal-models", "causal-inference", "causality-algorithms", "causality", "directed-acyclic-graph", "labeled-graphs", "graphs", "r", "c-plus-plus"]
}