src/datapro/data/skills/causal-inference/SKILL.md
Statistical modeling to isolate true drivers and associations. Use for: (1) Key Driver Analysis, (2) Calculating Chi-Square Residuals, (3) Plotting Association Matrices, (4) Partial Dependence Diagnostics.
npx skillsauth add pablodiegoo/data-pro-skill causal-inferenceInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
This skill utilizes mathematical modeling to go beyond correlation, identifying what genuinely moves the needle.
drivers_analysis.py: Identifies key drivers of a target variable (e.g., Overall Satisfaction).association_matrix.py: Calculates strong associations between categorical groups.chi2_residuals.py: Computes standard and adjusted residuals for cross-tabulations.partial_residual_plot.py & glm_partial_residual_plot.py: Validates linear and generalized linear models.factor_analysis.py: Discovers latent variables and structure in complex data.multivariate_normal_contours.py: Plots multidimensional relationships.testing
Comprehensive time-series validation and analysis suite. Handles backtesting of trading and non-trading strategies with support for walk-forward validation (training vs testing windows), performance metric calculation (Sharpe, Drawdown, Win Rate), and event-driven resource allocation simulation. Use for: (1) Validating sequential logic on time-series data, (2) Calculating risk-adjusted performance, (3) Simulating constraints in resource distribution, (4) Detecting look-ahead bias through walk-forward testing.
tools
Core statistical analysis and pipeline automation for survey datasets. Use for: (1) Running standard Crosstabs, NPS, Top-Box calculations, (2) Generating complete EDA or Analytics notebooks, (3) Quantitative and qualitative processing of questionnaire data.
development
Business-level frameworks and actionable reporting for executives. Use for: (1) Plotting Priority Matrices, (2) Generating Pain Curves, (3) Conversion Funnels, (4) Removing Halo Effects to uncover true sentiment.
testing
Tactical and highly interpretable Machine Learning. Use for: (1) Extracting Feature Importance via Random Forest, (2) Running Permutation Tests, (3) Handling Imbalanced Data (SMOTE).