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Morphology-based Entity and Relational Entity Extraction Framework for Arabic
Rule-based techniques to extract relational entities from documents allow users to specify desired entities with natural language questions, finite state automata, regular expressions and structured query language.
Causal Identification under Markov Equivalence
The problem of identification of causal effects is concerned with determining whether a causal effect can be computed from a combination of observational data and substantive knowledge about the domain under investigation, which is formally expressed in the form of a causal graph.
Identification of Conditional Causal Effects under Markov Equivalence
A generalization of this problem restricts the qualitative knowledge to a class of Markov equivalent causal diagrams, which, unlike a single, fully-specified causal diagram, can be inferred from the observational distribution.
Characterization and Learning of Causal Graphs with Latent Variables from Soft Interventions
We introduce a novel notion of interventional equivalence class of causal graphs with latent variables based on these invariances, which associates each graphical structure with a set of interventional distributions that respect the do-calculus rules.
Causal Discovery from Soft Interventions with Unknown Targets: Characterization and Learning
One fundamental problem in the empirical sciences is of reconstructing the causal structure that underlies a phenomenon of interest through observation and experimentation.
