mlsanityData to 7 Oct 2026

Generalized Random Forests

arXiv 1610.01271 ↗First posted 5 Oct 2016stat.ME econ.EM stat.MLcited by 309

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Abstract (arXiv)

We propose generalized random forests, a method for non-parametric statistical estimation based on random forests (Breiman, 2001) that can be used to fit any quantity of interest identified as the solution to a set of local moment equations. Following the literature on local maximum likelihood estimation, our method considers a weighted set of nearby training examples; however, instead of using classical kernel weighting functions that are prone to a strong curse of dimensionality, we use an adaptive weighting function derived from a forest designed to express heterogeneity in the specified quantity of interest. We propose a flexible, computationally efficient algorithm for growing generalized random forests, develop a large sample theory for our method showing that our estimates are consistent and asymptotically Gaussian, and provide an estimator for their asymptotic variance that enables valid confidence intervals. We use our approach to develop new methods for three statistical tasks: non-parametric quantile regression, conditional average partial effect estimation, and heterogeneous treatment effect estimation via instrumental variables. A software implementation, grf for R and C++, is available from CRAN.

Key facts. Cited by 309 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Oct 2016.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 42; 2022: 69; 2023: 58; 2024: 47; 2025: 43; 2026: 50.

309 papers.

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  1. 2021Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithmscited by 144
  2. 2023A Survey of Contextual Optimization Methods for Decision Making under Uncertaintycited by 81
  3. 2021On Inductive Biases for Heterogeneous Treatment Effect Estimationcited by 74
  4. 2024Causal machine learning for predicting treatment outcomescited by 73
  5. 2023GADBench: Revisiting and Benchmarking Supervised Graph Anomaly Detectioncited by 57
  6. 2021Conformalized Survival Analysiscited by 53
  7. 2022Criteria for Classifying Forecasting Methodscited by 48
  8. 2022Adversarial random forests for density estimation and generative modelingcited by 44
  9. 2025CausalPFN: Amortized Causal Effect Estimation via In-Context Learningcited by 43
  10. 2021RieszNet and ForestRiesz: Automatic Debiased Machine Learning with Neural Nets and Random Forestscited by 42
  11. 2021Causal Effect Inference for Structured Treatmentscited by 38
  12. 2023Data-OOB: Out-of-bag Estimate as a Simple and Efficient Data Valuecited by 38
38 more
  1. 2022Doubly Robust Distributionally Robust Off-Policy Evaluation and Learningcited by 34
  2. 2021Evaluating Treatment Prioritization Rules via Rank-Weighted Average Treatment Effectscited by 29
  3. 2023In Search of Insights, Not Magic Bullets: Towards Demystification of the Model Selection Dilemma in Heterogeneous Treatment Effect Estimationcited by 29
  4. 2021Doubly-Valid/Doubly-Sharp Sensitivity Analysis for Causal Inference with Unmeasured Confoundingcited by 28
  5. 2023B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confoundingcited by 28
  6. 2021SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Datacited by 25
  7. 2022Direct Heterogeneous Causal Learning for Resource Allocation Problems in Marketingcited by 21
  8. 2021DoWhy: Addressing Challenges in Expressing and Validating Causal Assumptionscited by 20
  9. 2022Validating Causal Inference Methodscited by 20
  10. 2022Image-based Treatment Effect Heterogeneitycited by 20
  11. 2022Neural Networks for Extreme Quantile Regression with an Application to Forecasting of Flood Riskcited by 19
  12. 2022Data-Driven Causal Effect Estimation Based on Graphical Causal Modelling: A Surveycited by 19
  13. 2022Robust and Agnostic Learning of Conditional Distributional Treatment Effectscited by 18
  14. 2021Prescriptive Process Monitoring for Cost-Aware Cycle Time Reductioncited by 17
  15. 2021Machine Learning for Variance Reduction in Online Experimentscited by 17
  16. 2022LBCF: A Large-Scale Budget-Constrained Causal Forest Algorithmcited by 17
  17. 2023Towards Causal Foundation Model: on Duality between Causal Inference and Attentioncited by 17
  18. 2023Causal Inference with Conditional Front-Door Adjustment and Identifiable Variational Autoencodercited by 17
  19. 2022Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretabilitycited by 15
  20. 2021A Tree-based Model Averaging Approach for Personalized Treatment Effect Estimation from Heterogeneous Data Sourcescited by 14
  21. 2021DoubleML -- An Object-Oriented Implementation of Double Machine Learning in Rcited by 13
  22. 2024Why do Random Forests Work? Understanding Tree Ensembles as Self-Regularizing Adaptive Smootherscited by 13
  23. 2021Robustifying Conditional Portfolio Decisions via Optimal Transportcited by 12
  24. 2021Prescriptive Process Monitoring Under Resource Constraints: A Causal Inference Approachcited by 12
  25. 2022Random Forest Weighted Local Fr'echet Regression with Random Objectscited by 12
  26. 2022A review of machine learning concepts and methods for addressing challenges in probabilistic hydrological post-processing and forecastingcited by 12
  27. 2023DRCFS: Doubly Robust Causal Feature Selectioncited by 12
  28. 2022Towards assessing agricultural land suitability with causal machine learningcited by 11
  29. 2022Falsification before Extrapolation in Causal Effect Estimationcited by 10
  30. 2023Instrumental Variable Estimation for Causal Inference in Longitudinal Data with Time-Dependent Latent Confounderscited by 10
  31. 2025TabPFN: One Model to Rule Them All?cited by 10
  32. 2022Survival Kernets: Scalable and Interpretable Deep Kernel Survival Analysis with an Accuracy Guaranteecited by 9
  33. 2023An End-to-End Framework for Marketing Effectiveness Optimization under Budget Constraintcited by 9
  34. 2024Decision Focused Causal Learning for Direct Counterfactual Marketing Optimizationcited by 9
  35. 2021Markdowns in E-Commerce Fresh Retail: A Counterfactual Prediction and Multi-Period Optimization Approachcited by 8
  36. 2022Binary Classification for High Dimensional Data using Supervised Non-Parametric Ensemble Methodcited by 8
  37. 2022Functional Generalized Empirical Likelihood Estimation for Conditional Moment Restrictionscited by 8
  38. 2022A Neural Mean Embedding Approach for Back-door and Front-door Adjustmentcited by 8

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