EconML User Guide

• Overview
• Machine Learning Based Estimation of Heterogeneous Treatment Effects
• Motivating Examples
  • Recommendation A/B testing
  • Customer Segmentation
  • Multi-investment Attribution
• Introduction to Causal Inference
• Problem Setup and API Design
  • API of Conditional Average Treatment Effect Package
  • Linear in Treatment CATE Estimators
  • Example Use of API
• Library Flow Chart
• Detailed estimator comparison
• Estimation Methods under Unconfoundedness
  • Orthogonal/Double Machine Learning
    • What is it?
    • What are the relevant estimator classes?
    • When should you use it?
    • Overview of Formal Methodology
    • Class Hierarchy Structure
    • Usage FAQs
    • Usage Examples
  • Doubly Robust Learning
    • What is it?
    • What are the relevant estimator classes?
    • When should you use it?
    • Overview of Formal Methodology
    • Class Hierarchy Structure
    • Usage FAQs
    • Usage Examples
  • Forest Based Estimators
    • What is it?
    • What are the relevant estimator classes?
    • When should you use it?
    • Overview of Formal Methodology
      • Orthogonal Random Forests
      • CausalForest (aka Forest Double Machine Learning)
      • Forest Doubly Robust Learner
    • Class Hierarchy Structure
    • Usage Examples
  • Meta-Learners
    • What is it?
    • What are the relevant estimator classes?
    • When should you use it?
    • Overview of Formal Methodology
      • T-Learner
      • S-Learner
      • X-Learner
      • Domain Adaptation Learner
      • Doubly Robust Learner
      • Non-Parametric Double Machine Learning
    • Class Hierarchy Structure
    • Usage Examples
• Estimation Methods with Instruments
  • Deep Instrumental Variables
  • Sieve 2SLS Instrumental Variable Estimation
  • Orthogonal instrumental variables
    • What is it?
    • What are the relevant estimator classes?
    • When should you use it?
    • Class Hierarchy Structure
    • Usage Examples
• Estimation Methods for Dynamic Treatment Regimes
  • Dynamic Double Machine Learning
    • What is it?
    • What are the relevant estimator classes?
    • When should you use it?
    • Class Hierarchy Structure
    • Usage FAQs
• Inference
  • Bootstrap Inference
  • OLS Inference
  • Debiased Lasso Inference
  • Subsampled Honest Forest Inference
  • OrthoForest Bootstrap of Little Bags Inference
• Model Selection
• Interpretability
  • Tree Interpreter
  • Policy Interpreter
  • SHAP
• Federated Learning in EconML
  • Overview
  • Motivation for Incorporating Federated Learning into the EconML Library
  • Federated Learning with EconML
    • Introducing the FederatedEstimator
    • Example Usage
  • Theory
• References
• Frequently Asked Questions (FAQ)
  • When should I use EconML?
  • What are the advantages of EconML?
  • How do I know if the results make sense?
  • I’m getting causal estimates that don’t make sense. What next?
  • What if I don’t have a good instrument, can’t run an experiment, and don’t observe all confounders?
  • How can I test whether I’m identifying the causal effect?
  • How do I give feedback?
• Community