DeepCausalMMM

Contents:

  • Installation
    • Requirements
    • Install from PyPI
    • Install from GitHub
    • Optional dependencies
    • Development Installation
    • Verify Installation
    • GPU Support
    • Docker Installation
    • Troubleshooting
  • Quick Start Guide
    • Basic Usage
    • One-Command Analysis
    • Data Format Requirements
    • Configuration Customization
    • NOTEARS DAG learning (optional)
    • Model Inference
    • Response Curves Analysis
    • Visualization and Analysis
    • Next Steps
    • Common Issues
  • Tutorials
    • End-to-End MMM Analysis
      • Overview
      • Step 1: Setup and Data Generation
        • Understanding the Data
      • Step 2: Configure the Model
        • Key Configuration Parameters
      • Step 3: Prepare Data Pipeline
        • What the Pipeline Does
      • Step 4: Create and Train Model
        • Training the Model
        • Understanding the Results
      • Step 5: Analyze Attribution
        • Channel-Level Attribution
      • Step 6: Fit Response Curves
        • Interpreting Response Curves
      • Step 7: Budget Optimization
        • Optimization Tips
      • Step 8: Save and Export Results
      • Complete Script
      • Next Steps
      • Troubleshooting
      • See Also
    • DAG and NOTEARS structure learning
      • Triangular mode (default)
      • NOTEARS mode (opt-in)
      • Key config keys
      • Training behaviour
      • Inspecting the learned graph
      • API reference
    • Available Tutorials
    • Coming Soon
  • API Reference
    • Core Components
      • Core Model Components
        • DeepCausalMMM Model
        • Configuration
        • DAG Model
        • Scaling
      • Data Processing
        • Unified Data Pipeline
      • Model Training
        • ModelTrainer
        • Training Functions
      • Model Inference
        • Inference Manager
    • Postprocessing and Analysis
      • Analysis and Postprocessing
        • Comprehensive Analysis
        • Statistical Analysis
        • DAG Postprocessing
      • Response Curves
        • Overview
        • Key Features
        • ResponseCurveFit Class
        • Basic Usage
        • Advanced Usage
        • Hill Equation
        • Technical Details
        • Backward Compatibility
        • Best Practices
        • Examples
        • See Also
      • Budget Optimization
        • Core Classes
        • Optimization Functions
        • Utility Functions
        • Usage Example
        • Optimization Methods
        • Response Curves
      • Visualization
        • VisualizationManager
    • Command Line Interface
      • Command Line Interface
        • main()
        • config_command()
        • version_command()
    • Utilities
      • Utilities
        • Device Management
        • Data Generation
      • Exceptions
        • Custom Exceptions
    • Complete API
      • deepcausalmmm
        • DeepCausalMMM: Deep Learning Marketing Mix Modeling with Causal Structure
        • DeepCausalMMM
        • get_default_config()
        • update_config()
        • ComprehensiveAnalyzer
        • ResponseCurveFit
        • ResponseCurveFitter
        • BudgetOptimizer
        • OptimizationResult
        • optimize_budget_from_curves()
        • SimpleGlobalScaler
        • GlobalScaler
        • get_device()
        • deepcausalmmm.cli
        • deepcausalmmm.core
        • deepcausalmmm.exceptions
        • deepcausalmmm.postprocess
        • deepcausalmmm.utils
  • Examples
    • Retail MMM analysis
    • Multi-region analysis
  • Contributing
    • Development Setup
    • Contributing Guidelines
      • Zero Hardcoding Philosophy
      • Code Quality Standards
      • Performance Standards
    • Submitting Changes
    • Documentation
    • Testing
    • Code Style
      • Naming Conventions
      • Import Organization
    • Recognition
    • Community Guidelines
    • Getting Help
DeepCausalMMM
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