correlation-auditor

Correlation analysis toolkit — computes Pearson/Spearman correlation matrices and partial correlation matrices from tabular data, and automatically flags suspected spurious correlations caused by confounding variables.

Capabilities

Feature	Description
Pearson Correlation Matrix	Linear correlation coefficients + p-values; suitable for continuous, approximately normal variables
Spearman Correlation Matrix	Rank correlation coefficients + p-values; suitable for nonlinear monotonic relationships or ordinal variables
Partial Correlation Matrix	Net correlations after controlling for all other variables (precision matrix method); reveals direct associations between variables
Spurious Correlation Detection	Automatically compares bivariate correlations with partial correlations and flags falsely significant correlations driven by confounders
Plain-Language Interpretation	Provides a readable summary of correlation strength, significance, and partial-correlation changes for each variable pair

Quick Start

# Analyze correlations across all numeric columns
python3 scripts/correlation_explorer.py data.csv