knowledge-graph-causal-discovery-mcp

> [View on ApifyForge](https://apifyforge.com/actors/mcp-servers/knowledge-graph-causal-discovery-mcp) | [Use on Apify Store](https://apify.com/ryanclinton/knowledge-graph-causal-discovery-mcp)

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Add to your MCP client (Claude Desktop, Cursor, Windsurf):

{
  "mcpServers": {
    "knowledge-graph-causal-discovery-mcp": {
      "url": "https://ryanclinton--knowledge-graph-causal-discovery-mcp.apify.actor/mcp"
    }
  }
}

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Knowledge graph causal discovery over multi-domain research data, delivered through a single Model Context Protocol interface. This MCP server is built for researchers, data scientists, and AI agents that need to go beyond correlation — discovering directed causal structure, estimating treatment effects, and reasoning about counterfactuals from the published literature and public datasets.

The server orchestrates 17 Apify actors in parallel across five source domains — academic, biomedical, regulatory, economic, and safety — assembling the results into a unified causal knowledge graph. Eight specialized tools then apply rigorous causal inference algorithms: FCI skeleton learning, GES with BIC scoring, Pearl's do-calculus with the ID algorithm, twin network counterfactuals, TMLE estimation, RotatE knowledge graph embeddings, sheaf cohomology consistency checking, and Shapley source attribution. Every tool call returns structured JSON with mathematical scores and supporting evidence.

| Data Point | Source | Coverage | |---|---|---| | 📄 Academic papers and citations | OpenAlex, Semantic Scholar, Crossref | 250M+ scholarly works with citation graphs | | 📑 Preprints and open access | arXiv, CORE | Physics, CS, quantitative biology, math | | 🧬 Biomedical literature | PubMed | 36M+ citations with MeSH indexing | | 🏥 Clinical trials | ClinicalTrials.gov | 450K+ registered studies with protocol data | | 💊 Drug adverse event repo