Examples

Working example notebooks are available in the example folder.

Follow the below links for an approximate ordering of example tutorials from introductory to advanced features.

• Meta-Learners Examples - Training, Estimation, Validation, Visualization
• Uplift Trees Example with Synthetic Data
• Meta-Learners Examples - Single/Multiple Treatment Cases
• Uplift Trees/Forests Visualization
• Model Interpretation with Feature Importance and SHAP Values
• Uplift Curves with TMLE Example
• DragonNet vs Meta-Learners Benchmark with IHDP + Synthetic Datasets
• 2SLS Benchmarks with NLSYM + Synthetic Datasets
• Sensitivity Analysis Examples
• Unit Selection Based on Counterfactual Logic by Li and Pearl (2019)
• Counterfactual Value Estimation Using Outcome Imputation by Li and Pearl (2019)
• Feature Selection for Uplift Trees by Zhao et al. (2020)
• Policy Learner by Athey and Wager (2018) with Binary Treatment
• CEVAE vs. Meta-Learners Benchmark with IHDP + Synthetic Datasets
• DR Learner vs. DR-IV Learner vs. X-Learner Benchmark with Synthetic Data
• Meta-Learner Benchmarks with Synthetic Data in Nie and Wager (2020)
• Causal Trees/Forests Treatment Effects Estimation and Tree Visualization
• Causal Trees/Forests Interpretation with Feature Importance and SHAP Values
• Logistic Regression Based Data Generation Function for Uplift Classification Problem
• Qini curves with multiple costly treatment arms
• Propensity Score Calibration
• Modify above example to use IsotonicRegression
• Meta-Learner Benchmarks with Semi-synthetic Data in Schuler, A., Jung, K., Tibshirani, R., Hastie, T., and Shah, N. Synth-validation: Selecting the best causal inference method for a given dataset (2017)