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name: correlation-auditor description: "Analyzes correlation matrices (Pearson/Spearman), computes partial correlations to control for confounding variables, and flags potential spurious correlations in your data. Triggered when users ask about relationships between variables, need correlation matrices, or mention Pearson/Spearman coefficients, partial correlation, confounding factors, or spurious correlations." license: MIT

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

# Analyze only specific columns
python3 scripts/correlation_explorer.py data.csv -f "age,income,spending,score"

# Compute Pearson only
python3 scripts/correlation_explorer.py data.csv -m pearson

# Save results to JSON
python3 scripts/correlation_explorer.py data.csv -o result.json

Detailed Usage

Basic Invocation

python3 scripts/correlation_explorer.py <data-file> [options]

Choosing the Correlation Method

# Compute both Pearson and Spearman (default)
python3 scripts/correlation_explorer.py data.csv -m all

# Pearson only
python3 scripts/correlation_explorer.py data.csv -m pearson

# Spearman only
python3 scripts/correlation_explorer.py data.csv -m spearman

Tuning Spurious-Correlation Detection Sensitivity

# Stricter: alert when the coefficient drops by 30%
python3 scripts/correlation_explorer.py data.csv -d 0.3

# More lenient: alert only when the coefficient drops by 70%
python3 scripts/correlation_explorer.py data.csv -d 0.7

# Use a 0.01 significance level
python3 scripts/correlation_explorer.py data.csv -a 0.01

Parameters

Parameter	Short	Required	Default	Description
input	—	Yes	—	Input file path (CSV/TSV/Excel/JSON)
--features	-f	No	All numeric columns	Column names to analyze, comma-separated
--method	-m	No	all	Correlation method: all / pearson / spearman
--alpha	-a	No	0.05	Significance level
--drop-threshold	-d	No	0.5	Drop threshold for spurious-correlation detection (0–1; default 50%)
--output	-o	No	stdout	Output JSON file path (prints to stdout if omitted)

Output Structure (JSON)

{
  "n_observations": 200,
  "n_variables": 4,
  "features": ["age", "income", "spending", "score"],
  "pearson": {
    "columns": ["age", "income", "spending", "score"],
    "correlation": [[1.0, 0.72, ...], ...],
    "p_values": [[0.0, 0.0001, ...], ...]
  },
  "spearman": { "..." : "same structure as pearson" },
  "partial_correlation": {
    "columns": ["age", "income", "spending", "score"],
    "partial_correlation": [[1.0, 0.15, ...], ...],
    "p_values": [[0.0, 0.32, ...], ...],
    "df": 196
  },
  "spurious_correlations": [
    {
      "var_x": "age",
      "var_y": "spending",
      "pearson_r": 0.65,
      "partial_r": 0.08,
      "drop_pct": 87.7,
      "reasons": ["partial correlation not significant", "coefficient dropped by 87.7%"]
    }
  ],
  "interpretation": {
    "overview": ["Analyzed correlations among 4 variables..."],
    "strongest_pairs": ["income <-> spending: r = 0.82 (very strong positive)"],
    "partial_correlation_insights": ["age <-> spending: weakened by 87.7% after controlling for other variables"],
    "spurious_correlation_check": ["Found 1 suspected spurious correlation pair..."]
  }
}

Key Concepts

Partial Correlation vs. Bivariate Correlation

• Bivariate correlation (Pearson/Spearman): The overall association between two variables, which may be inflated by the influence of a third variable
• Partial correlation: The "net" association between two variables after controlling for all others
• If the partial correlation is substantially smaller than the bivariate correlation, the observed association is largely mediated or confounded by other variables

Spurious Correlation

Two variables may appear correlated only because both are influenced by a shared confounding variable. This tool automatically identifies such cases by comparing bivariate and partial correlations.

Dependencies

• Python 3.8+
• pandas
• numpy
• scipy

pip install pandas numpy scipy