StatsPAI: Causal Inference Made Easy
StatsPAI provides a unified API for causal inference and applied econometrics, allowing data-driven founders to easily perform complex statistical analyses.
- •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
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
Skip the builder — one click puts this in Claude, Cursor, Antigravity and more.
Installs with a command or two; your AI agent can do it for you.
mkdir -p ~/.claude/skills/statspai && curl -fsSL https://workflowstacks.com/api/skills/statspai/claude-skill -o ~/.claude/skills/statspai/SKILL.mdOpens the app with this repo with the prompt ready to go — no copy-paste needed.
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.
- 1README.mdStart here — what it does and how to install it
- 2CLAUDE.mdThe instructions the AI actually follows
- 3pyproject.tomlDependencies and the commands it exposes
- 4src/statspai/__init__.pyInside src/ — the main logic begins here
- 5examples/README.mdA worked example — copy this to get going
Skip the builder — one click puts this in Claude, Cursor, Antigravity and more.
Installs with a command or two; your AI agent can do it for you.
mkdir -p ~/.claude/skills/statspai && curl -fsSL https://workflowstacks.com/api/skills/statspai/claude-skill -o ~/.claude/skills/statspai/SKILL.mdOpens the app with this repo with the prompt ready to go — no copy-paste needed.
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