# Causal Inference MCP Server

> Causal Inference MCP Server — vinit-gautam-22-casual-mcp-server. Use this tool when you need to perform causal inference analyses, such as estimating treatment effects or comparing outcomes, to solve problems like evaluating policy interventions or understanding cause-and-effect relationships. It takes in data and method parameters as inputs and outputs statistical results, enabling AI agents to make informed decisions. Ideal for use cases where deterministic statistical analyses are required, such as policy evaluation or program assessment.

Canonical page: https://skillsregistry.net/skills/vinit-gautam-22-casual-mcp-server  
JSON: https://api.skillsregistry.net/v1/skills/vinit-gautam-22-casual-mcp-server

## Description

An MCP server that exposes causal inference methods (difference-in-differences, synthetic control, propensity matching, and assumption checks) as callable tools, enabling AI agents to run deterministic statistical analyses instead of computing them inline.

## Trust

- **Trust score (0–1):** 0.70
- **Verification tier:** verified
- **Last scanned:** 2026-08-29

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** ai-ml
- **Updated:** 2026-08-29

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/r1wwy9jdi2)
- **Repository:** <https://github.com/Vinit-Gautam-22/Casual-mcp-server>

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "vinit-gautam-22-casual-mcp-server"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/vinit-gautam-22-casual-mcp-server` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/vinit-gautam-22-casual-mcp-server/pull`

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SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt