[RnD] Variance Reduction

[tikhomirovd]

🚀 Feature Proposal

Public Research on Various Methods for Variance Reduction

Motivation

Currently, the HypEx library employs multiple approaches for data analysis, including variance reduction techniques. However, there is no comprehensive research available that compares different variance reduction methods. Conducting such a study and publishing it as a tutorial will help users understand which methods are best suited for their tasks, increasing transparency and trust in results obtained using the library.

Feature Description

• Conduct research on different variance reduction techniques, including bootstrap, stratification, weighting, meta-analytical methods, etc.
• Compare these methods on different types of data and tasks (A/B testing, causal inference, predictive modeling).
• Present findings in a detailed Jupyter Notebook containing code, visualizations, and explanations.
• Publish the notebook in the tutorials section of the documentation.

Potential Impacts

• Improved user understanding of variance reduction techniques.
• Enhanced credibility of analysis results produced with HypEx.
• Increased adoption of best practices for statistical modeling.

Alternatives

• Providing a simple theoretical explanation without code examples.
• Linking to external research papers instead of conducting an in-house study.

Additional Context

• Ensure that the tutorial follows the documentation guidelines of HypEx.
• Use relevant datasets to demonstrate practical applications of each method.

Checklist

• Conduct research on variance reduction methods.
• Implement code examples and comparisons.
• Create a Jupyter Notebook tutorial.
• Add the tutorial to the tutorials section.
• Review and refine the tutorial before publication.