ConversionFlow

Contents:

  • Getting Started with ConversionFlow
  • How-to Guides
  • Explanation
    • Estimation Process
    • Optimisation Process
    • Bayesian Networks in ConversionFlow
    • Causal Chain Analysis
    • Model Complexity Considerations
  • Reference
  • Troubleshooting
  • Testing
ConversionFlow
  • Explanation
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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
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