Overview

Causal Inference uses mathematical modeling to go beyond correlation and isolate what genuinely moves the needle. It supports driver and association analysis, chi-square residuals, and partial dependence diagnostics to validate relationships and reveal latent structure in complex datasets.

How This Skill Works

Core components drive the workflow: drivers_analysis.py finds key drivers of a target (e.g., Overall Satisfaction), association_matrix.py measures strong relationships between categorical groups, and chi2_residuals.py computes cross-tab residuals. For modeling and diagnostics, partial_residual_plot.py and glm_partial_residual_plot.py validate linear and generalized linear models. Factor_analysis.py and multivariate_normal_contours.py reveal latent structure and multidimensional relationships.

When to Use It

• When you need to identify which factors truly drive a target metric (Key Driver Analysis).
• When you want to assess cross-tab fit using chi-square residuals.
• When exploring strong associations between categorical groups with an association matrix.
• When validating linear/GLM models with partial residual plots.
• When uncovering latent structure or multidimensional relationships with factor analysis and contour plots.

Quick Start

1. Step 1: Define your target variable and run drivers_analysis.py to identify key drivers.
2. Step 2: Run association_matrix.py and chi2_residuals.py to map associations and assess cross-tab fit.
3. Step 3: Use partial_residual_plot.py or glm_partial_residual_plot.py to validate models; optionally run factor_analysis.py and multivariate_normal_contours.py for latent structure.

Best Practices

• Define a clear target variable before analysis.
• Pre-clean data and ensure sufficient category counts for chi-square tests.
• Use drivers_analysis to rank drivers, then corroborate with association and residual analyses.
• Regularly validate models with partial_residual_plot.py and glm_partial_residual_plot.py.
• Leverage factor_analysis.py and multivariate_normal_contours.py to explore latent structure.

Example Use Cases

• Use drivers_analysis.py to identify key drivers of Overall Satisfaction in a customer survey.
• Apply chi2_residuals.py to compute residuals for a cross-tab of product category by region.
• Plot association_matrix.py to reveal strong associations between customer segments.
• Validate a revenue prediction model with partial_residual_plot.py.
• Discover latent segments using factor_analysis.py and visualize with multivariate_normal_contours.py.

Frequently Asked Questions