mcp-server

StatsPAI: Causal Inference Made Easy

Get unified causal inference with StatsPAI, a Python library for data-driven founders.
298 stars64 forksPythonGuide quality 8/10Updated 8/11/2026100% free · open source
What it does

StatsPAI provides a unified API for causal inference and applied econometrics, allowing data-driven founders to easily perform complex statistical analyses.

When to use it
  • When you need to perform causal inference on your startup's data to inform business decisions
  • When you want to unify different statistical methods under a single API for easier comparison and analysis
  • When you require machine-readable schemas and structured result objects for further data processing and visualization
Ready-to-paste prompt
Run `statspai.CausalModel(y='outcome', T='treatment', X=['covariate1', 'covariate2']).estimate()` to estimate the causal effect of a treatment on an outcome while controlling for covariates
Heads up: Make sure you have Python 3.8 or later installed, as StatsPAI is not compatible with earlier Python versions
Saves to your device
Use with Claude
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Skip the builder — one click puts this in Claude, Cursor, Antigravity and more.

✅ Light setup

Installs with a command or two; your AI agent can do it for you.

Try it instantly — no install
Claude Code
mkdir -p ~/.claude/skills/statspai && curl -fsSL https://workflowstacks.com/api/skills/statspai/claude-skill -o ~/.claude/skills/statspai/SKILL.md
Open in another AI app

Opens the app with this repo with the prompt ready to go — no copy-paste needed.

Connect the whole catalog (MCP)
claude mcp add --transport http workflowstacks https://workflowstacks.com/api/mcp

Adds a WorkflowStacks connector to Claude Code: search and load any skill here by chatting.

How StatsPAI: Causal Inference Made Easy works
Codeflow
Free to inspect

StatsPAI: Causal Inference Made Easy is a very large Python project (~370k lines across 830 code files, plus 1388 test files). Setup is light: installs like a normal app. Reading the code is optional. Last commit this month, MIT license, has a test suite.

Size
Very large codebase
~370k lines · 830 code files · days to read — use, don't read
Setup
One-command install
Installs like a normal app. Reading the code is optional.
Runs on
Python
No API keys detected
Python 94%TeX 2%R 2%Stata 1%Jupyter Notebook 1%
Where to start reading
  1. 1
    README.md
    Start here — what it does and how to install it
  2. 2
    CLAUDE.md
    The instructions the AI actually follows
  3. 3
    pyproject.toml
    Dependencies and the commands it exposes
  4. 4
    src/statspai/__init__.py
    Inside src/ — the main logic begins here
  5. 5
    examples/README.md
    A worked example — copy this to get going
What's in each folder
src/Core code — the actual logic774 files
StatsPAI_full_data_analysis_skill/Prompts, skills & agent definitions4 files
docs/Documentation161 files
examples/Examples you can copy10 files
benchmarks/Evaluations & benchmarks59 files
scripts/Helper scripts36 files
plans/Folder18 files
rust/Folder11 files
READMEHas testsDocumentedCI checksExamples includedMIT licenseUpdated this month
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Details
Creator
brycewang-stanford
Language
Python
Category
mcp-server
Published
7/26/2025

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