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 from PyPI:
  • Install from source:
• API Quickstart
  • Propensity Score
  • Average Treatment Effect (ATE) Estimation
  • More algorithms
  • Interpretation
  • Validation
  • Synthetic Data Generation Process
  • Sensitivity Analysis
  • Feature Selection
• Examples
  • Meta-Learner Benchmarks with Synthetic Data in Nie and Wager (2020)
  • Policy Learner by Athey and Wager (2018) with Binary Treatment
  • Causal Trees/Forests Interpretation with Feature Importance and SHAP Values
  • Causal Trees/Forests Treatment Effects Estimation and Tree Visualization
  • CEVAE vs. Meta-Learners Benchmark with IHDP + Synthetic Datasets
  • Unit Selection Based on Counterfactual Logic by Li and Pearl (2019)
  • Counterfactual Value Estimation Using Outcome Imputation by Li and Pearl (2019)
  • DR Learner vs. DR-IV Learner vs. X-Learner Benchmark with Synthetic Data
  • DragonNet vs Meta-Learners Benchmark with IHDP + Synthetic Datasets
  • Model Interpretation with Feature Importance and SHAP Values
  • Feature Selection for Uplift Trees by Zhao et al. (2020)
  • 2SLS Benchmarks with NLSYM + Synthetic Datasets
  • Meta-Learners Examples - Training, Estimation, Validation, Visualization
  • Meta-Learners Examples - Single/Multiple Treatment Cases
  • Sensitivity Analysis Examples
  • Uplift Trees/Forests Visualization
  • Uplift Trees Example with Synthetic Data
  • Uplift Curves with TMLE Example
  • Logistic Regression Based Data Generation Function for Uplift Classification Problem
• 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
  • 0.14.1 (Aug 2023)
  • 0.14.0 (July 2023)
  • 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