serejaris 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