Welcome to Causal ML’s documentation

Contents:

• About Causal ML
• Methodology
  • Meta-Learner Algorithms
  • Tree-Based Algorithms
  • Value optimization methods
  • Probabilities of causation
  • Selected traditional methods
  • Targeted maximum likelihood estimation (TMLE) for ATE
• Installation
  • Install using conda
  • Install using pip
  • Install from source
• Examples
  • Propensity Score
  • Average Treatment Effect (ATE) Estimation
  • More algorithms
  • Interpretation
  • Validation
  • Synthetic Data Generation Process
  • Sensitivity Analysis
  • Feature Selection
• Interpretable Causal ML
  • Meta-Learner Feature Importances
  • Uplift Tree Visualization
  • Uplift Tree Feature Importances
• Validation
  • Validation with Multiple Estimates
  • Validation with Synthetic Data Sets
  • Validation with Uplift Curve (AUUC)
  • Validation with Sensitivity Analysis
• causalml package
  • Submodules
  • causalml.inference.tree module
  • causalml.inference.meta module
  • causalml.inference.iv module
  • causalml.inference.nn module
  • causalml.inference.tf module
  • causalml.optimize module
  • causalml.dataset module
  • causalml.match module
  • causalml.propensity module
  • causalml.metrics module
  • causalml.feature_selection module
  • causalml.features module
  • Module contents
• References
  • Open Source Software Projects
  • Papers
• Changelog
  • 0.13.0 (Sep 2022)
  • 0.12.3 (Feb 2022)
  • 0.12.2 (Feb 2022)
  • 0.12.1 (Feb 2022)
  • 0.12.0 (Jan 2022)
  • 0.11.0 (2021-07-28)
  • 0.10.0 (2021-02-18)
  • 0.9.0 (2020-10-23)
  • 0.8.0 (2020-07-17)
  • 0.7.1 (2020-05-07)
  • 0.7.0 (2020-02-28)
  • 0.6.0 (2019-12-31)
  • 0.5.0 (2019-11-26)
  • 0.4.0 (2019-10-21)
  • 0.3.0 (2019-09-17)
  • 0.2.0 (2019-08-12)
  • 0.1.0 (unreleased)

Indices and tables

• Index

• Module Index

• Search Page