A statistical framework for quantifying the relative importance of multiple factors in explaining the variance of an outcome variable.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install lake-warming-attribution-contribution-analysis
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install lake-warming-attribution-contribution-analysis using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Contribution Analysis?
The Contribution Analysis skill provides a robust methodology for determining which variables drive results in complex datasets. By leveraging R² decomposition and Principal Component Analysis (PCA), this workflow within Openclaw Skills allows developers and data scientists to move beyond simple correlation to understand true impact.
It specifically focuses on decomposing the coefficient of determination (R²) to identify how much each group of variables contributes to the overall explanatory power of a model. This is essential for interpreting complex models where variables are highly correlated or belong to distinct functional categories.
Contribution Analysis Use Cases
- Quantifying marketing channel impact on total sales revenue.
- Identifying the primary drivers of user churn in SaaS applications.
- Analyzing which hardware specifications most influence system latency in cloud environments.
- Evaluating the relative contribution of different socioeconomic factors to public health outcomes.
How Contribution Analysis Works
- Consolidate all potential explanatory variables and the response variable into a single matrix for processing.
- Standardize the data using a standard scaler to ensure uniform variance across different feature scales.
- Perform a global Principal Component Analysis (PCA) using a Factor Analyzer with varimax rotation to identify underlying latent factors.
- Calculate the full model R² by training a linear regression on all extracted factor scores.
- Systematically remove individual factors and recalculate R² to determine the marginal contribution (the loss in variance explanation) of each specific factor.
- Map the factor contributions to high-level categories and export the results to identify the dominant driver.
Contribution Analysis Setup
To utilize this contribution analysis workflow in your local environment, ensure you have the necessary Python data science stack installed. You can set up the requirements for this part of Openclaw Skills using the following command:
pip install pandas numpy scikit-learn factor_analyzer
Ensure your dataset is formatted as a pandas DataFrame before passing it into the analysis script.
Contribution Analysis Data Schema & Taxonomy
The skill processes input dataframes and produces structured analytical outputs based on the following schema:
| Data Point | Type | Description |
|---|---|---|
| Independent Variables | Numeric Matrix | The raw features or variables used for factor extraction. |
| Response Variable | Numeric Vector | The target outcome variable being explained. |
| Factor Loadings | Matrix | The relationship between original variables and extracted factors. |
| Contributions | Dictionary | A mapping of category names to their percentage contribution to R². |
| output.csv | CSV File | A file containing the dominant variable and its calculated percentage impact. |
Contribution Analysis Advanced Features
- Varimax rotation implementation for improved factor interpretability and category mapping.
- Support for high-dimensionality datasets through global PCA preprocessing within Openclaw Skills.
- Automated identification of the dominant driver for rapid decision-making and reporting.
- Resilient handling of multicollinearity through factor score decomposition rather than raw variable regression.
- Standardized output generation compatible with downstream visualization tools.
SKILL.md
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