This powerful MCP server offers a suite of advanced capabilities for constructing knowledge graphs and unveiling causal relationships hidden within complex, multi-source datasets. It facilitates robust entity resolution, precise relationship mapping, and deep causal inference, providing tools to discover causal structures, compute interventional effects, simulate counterfactuals, and extract causal claims from literature, all essential for sophisticated AI and research applications.
Key Features
01Compute interventional effects P(Y|do(X)) via do-calculus
02Discover causal structure via FCI with KCI tests
03Simulate counterfactual outcomes via twin network method
04Embed causal knowledge graph via RotatE embeddings
05Extract causal claims from academic literature via NLP
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