• VERIFIED
  • REJECTED
  • HUMAN
  • MODEL

How to audit causal root cause analysis with your AI

Connect your AI to Isaac Newton Prover to move beyond simple pattern matching. Your agent will start testing actual causal links instead of just spotting coincidences.

Ask AI about this page

Short answer

Can my AI tell the difference between correlation and causation?

Yes, if you give it the right tools. By using this MCP, your AI can run formal causal tests to see if one variable actually forces a change in another. You get a breakdown of true drivers versus mere coincidences.

The audit results

What you get from the analysis

The AI processes your system data to isolate the real culprits.

VERIFIED

Confirmed causal drivers

The AI identifies variables that actually trigger changes in your system. You get a list of true root causes.

REJECTED

Filtered correlations

The agent strips away coincidental patterns that look important but have no functional link to the outcome.

HUMAN

Expert intervention points

The AI flags complex or ambiguous causal paths where the math is uncertain. You decide how to act on these edge cases.

MODEL

Structural causal models

Your agent builds a map of how variables interact. This shows you the underlying logic of your system's behavior.

The workflow

What your AI does when the data arrives.

The AI moves from observing patterns to testing the mechanics of your system.

  1. Identify patterns

    Your agent scans your datasets to find variables that move together.

    validate_isaac_newton

  2. Test causal links

    The AI runs tests to see if changing one variable actually forces a response in another.

    validate_isaac_newton

  3. Isolate drivers

    The agent separates the true causes from the noise and coincidental data points.

    validate_isaac_newton

  4. Map the system

    Your agent produces a structural view of how these forces interact.

    validate_isaac_newton

Try it

Copy these to start.

Use these prompts to begin your audit.

Starting points

Replace the bracketed text with your specific dataset or system description.

Isaac Newton Prover Connector

You're all set. Choose your MCP client and follow the setup instructions.

Connector linkhttps://edge.vinkius.com/vk_preview_vTChG7gwDRonkMUYzv5YVRG07gLFRjm6ieiSUcNA/mcp

Claude Desktop

Follow the steps below to connect in seconds.

  1. 1In Claude Desktop, open Settings → Connectors.
  2. 2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
  3. 3Click Add and start a new chat — Isaac Newton Prover capabilities are ready to use.
Configuration · claude_desktop_config.jsonCopy
{
  "mcpServers": {
    "isaac-newton-prover-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_vTChG7gwDRonkMUYzv5YVRG07gLFRjm6ieiSUcNA/mcp"
    }
  }
}
Copy into chat04

  • Audit the correlation between user churn and login frequency in my 'customer_metrics' sheet.

  • Check if the recent server latency in my 'system_logs' is actually caused by the database load.

  • Verify the causal relationship between marketing spend and sales in this 'q3_revenue' CSV.

  • Analyze my 'manufacturing_sensor_data' to find the real root cause of the recent equipment downtime.

  • Claude
  • ChatGPT
  • Cursor
  • VS Code
  • Windsurf
  • Claude Code
  • JetBrains
  • Cline

Start here

Connect Isaac Newton Prover once, then ask.

One click connects this MCP to your AI client. Your credentials stay encrypted, and you can start auditing immediately.

Connect Isaac Newton Prover to your AI

FAQ

How this task behaves

  • 01

    Can the AI change my original data files?

    No. The AI only reads your data to perform analysis. It cannot modify, delete, or overwrite your source files.

  • 02

    Does the AI make the final decision on what caused the issue?

    No. The AI provides the mathematical proof and identifies the likely drivers, but you must interpret the results and decide on the action.

  • 03

    What kind of data can I use for this?

    You can use any structured data like CSVs, spreadsheets, or database exports that your AI client can access.

  • 04

    How does it handle data that is just random noise?

    The tool identifies these as mere correlations and excludes them from the list of true causal drivers.

  • 05

    Can the AI run these tests on live streaming data?

    No. This tool is designed for auditing existing datasets and logs to find causes after an event has occurred.

  • More questions about Isaac Newton Prover? The Connector page answers them. See everything the Isaac Newton Prover Connector can do

Connect Isaac Newton Prover to Claude, Cursor, ChatGPT & more