Auto Causal Inference
This MCP server provides automated causal inference capabilities by combining LLM-guided variable classification with DoWhy statistical analysis on SQLite banking data. Built with Python using FastMCP, LangGraph, and OpenAI's GPT-3.5-turbo, it features a single tool that takes treatment and outcome variables, uses AI to classify other variables as confounders, mediators, effect modifiers, colliders, or instruments, generates causal graphs in DOT format, and executes DoWhy code to estimate Average Treatment Effects using backdoor adjustment with linear regression. The implementation includes both standalone agent and MCP server versions, automatically generates plain-language business summaries of causal effects, and works with predefined banking variables like customer demographics, engagement metrics, and Internet Banking activation, making it valuable for business analysts exploring causal relationships in customer data without requiring deep statistical expertise.
Composite of vulnerability cleanliness, spec conformance, provenance, stability, and usage signals — scanned and weighted by Cognium. Human and agent signals are tracked separately. Last scanned 2026-09-28.
Scan details: Circle-IR · 2026-09-28 · Appeal
View full trust & usage report →Metadata
- Version
- 1.0.0
- Skill type
- atomic
- Execution layer
- mcp-remote
- Category
- database
- Source
- PulseMCP
- Author type
- human
- Last scanned
- 2026-09-28
- Updated
- 2026-09-28
Use via MCP
Resolve Auto Causal Inference from your agent
Streamable HTTP transport at https://api.skillsregistry.net/mcp. No auth for read tools. Discovery: .well-known/mcp.json.
One command in your shell — Claude Code wires it up and verifies the connection. Run /mcp in any session to confirm.
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp--scope user for --scope project to commit it to .mcp.json.