Welcome to econml’s documentation!

• 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
• Public Module Reference
  • CATE Estimators
    • Double Machine Learning (DML)
      • econml.dml.DML
      • econml.dml.LinearDML
      • econml.dml.SparseLinearDML
      • econml.dml.CausalForestDML
      • econml.dml.NonParamDML
      • econml.dml.KernelDML
    • Doubly Robust (DR)
      • econml.dr.DRLearner
      • econml.dr.LinearDRLearner
      • econml.dr.SparseLinearDRLearner
      • econml.dr.ForestDRLearner
    • Meta-Learners
      • econml.metalearners.XLearner
      • econml.metalearners.TLearner
      • econml.metalearners.SLearner
      • econml.metalearners.DomainAdaptationLearner
    • Orthogonal Random Forest (ORF)
      • econml.orf.DMLOrthoForest
      • econml.orf.DROrthoForest
  • Instrumental Variable CATE Estimators
    • Double Machine Learning (DML) IV
      • econml.iv.dml.OrthoIV
      • econml.iv.dml.DMLIV
      • econml.iv.dml.NonParamDMLIV
    • Doubly Robust (DR) IV
      • econml.iv.dr.DRIV
      • econml.iv.dr.LinearDRIV
      • econml.iv.dr.SparseLinearDRIV
      • econml.iv.dr.ForestDRIV
      • econml.iv.dr.IntentToTreatDRIV
      • econml.iv.dr.LinearIntentToTreatDRIV
    • DeepIV
      • econml.iv.nnet.DeepIV
    • Sieve Methods
      • econml.iv.sieve.SieveTSLS
      • econml.iv.sieve.HermiteFeatures
      • econml.iv.sieve.DPolynomialFeatures
  • Estimators for Panel Data
    • Dynamic Double Machine Learning
      • econml.panel.dml.DynamicDML
  • Policy Learning
    • econml.policy.DRPolicyForest
    • econml.policy.DRPolicyTree
    • econml.policy.PolicyForest
    • econml.policy.PolicyTree
  • CATE Interpreters
    • econml.cate_interpreter.SingleTreeCateInterpreter
    • econml.cate_interpreter.SingleTreePolicyInterpreter
  • CATE Validation
    • econml.validate.DRTester
    • econml.validate.BLPEvaluationResults
    • econml.validate.CalibrationEvaluationResults
    • econml.validate.UpliftEvaluationResults
    • econml.validate.EvaluationResults
  • CATE Scorers
    • econml.score.RScorer
    • econml.score.EnsembleCateEstimator
  • Generalized Random Forests
    • econml.grf.CausalForest
    • econml.grf.CausalIVForest
    • econml.grf.RegressionForest
    • econml.grf.MultiOutputGRF
    • econml.grf.LinearMomentGRFCriterion
    • econml.grf.LinearMomentGRFCriterionMSE
    • econml.grf._base_grf.BaseGRF
    • econml.grf._base_grftree.GRFTree
  • Scikit-Learn Extensions
    • Linear Model Extensions
      • econml.sklearn_extensions.linear_model.DebiasedLasso
      • econml.sklearn_extensions.linear_model.MultiOutputDebiasedLasso
      • econml.sklearn_extensions.linear_model.SelectiveRegularization
      • econml.sklearn_extensions.linear_model.StatsModelsLinearRegression
      • econml.sklearn_extensions.linear_model.StatsModelsRLM
      • econml.sklearn_extensions.linear_model.WeightedLasso
      • econml.sklearn_extensions.linear_model.WeightedLassoCV
      • econml.sklearn_extensions.linear_model.WeightedMultiTaskLassoCV
      • econml.sklearn_extensions.linear_model.WeightedLassoCVWrapper
    • Model Selection Extensions
      • econml.sklearn_extensions.model_selection.GridSearchCVList
      • econml.sklearn_extensions.model_selection.WeightedKFold
      • econml.sklearn_extensions.model_selection.WeightedStratifiedKFold
  • Inference
    • Inference Results
      • econml.inference.NormalInferenceResults
      • econml.inference.EmpiricalInferenceResults
      • econml.inference.PopulationSummaryResults
    • Inference Methods
      • econml.inference.BootstrapInference
      • econml.inference.GenericModelFinalInference
      • econml.inference.GenericSingleTreatmentModelFinalInference
      • econml.inference.LinearModelFinalInference
      • econml.inference.StatsModelsInference
      • econml.inference.GenericModelFinalInferenceDiscrete
      • econml.inference.LinearModelFinalInferenceDiscrete
      • econml.inference.StatsModelsInferenceDiscrete
  • Federated Estimation
    • econml.federated_learning.FederatedEstimator
  • Solutions
    • Causal Analysis
      • econml.solutions.causal_analysis.CausalAnalysis
  • Integration with DoWhy
    • econml.dowhy.DoWhyWrapper
  • Utilities
    • econml.utilities
• Private Module Reference
  • econml._ortho_learner
  • econml._cate_estimator
  • econml.dml._rlearner
  • econml.inference._bootstrap

Indices and tables

• Index

• Module Index

• Search Page