Explanation

This section provides in-depth explanations of the concepts and theoretical foundations of the ConversionFlow library.

Contents:

• Estimation Process
  • Overview
  • Data Loading
  • Data Preprocessing
  • Bayesian Network Model Inference
• Optimisation Process
  • Problem Statement
  • Mathematical Formulation
  • Optimisation Algorithm: Genetic Algorithm
  • Implementation Details
• Bayesian Networks in ConversionFlow
  • Conceptual Foundation
  • Key Principles
  • Application in ConversionFlow
  • Advantages Over Traditional Approaches
  • Limitations and Considerations
• Causal Chain Analysis
  • Fundamentals of Causality in Conversion Flow
  • The Causal Chain Concept
  • Causal Parameters in ConversionFlow
  • Identifying Causal Bottlenecks
  • Multi-path Causal Analysis
  • Causal Attribution
  • Practical Applications
  • Limitations and Challenges
  • Future Directions
• Model Complexity Considerations
  • Understanding Model Complexity in ConversionFlow
  • Dimensions of Model Complexity
  • Model Selection Strategies
  • Balancing Complexity and Utility
  • Case Study: Evolution of ConversionFlow Models
  • Future Directions in Model Complexity