mlsanityData to 7 Oct 2026

Distributional Random Forests: Heterogeneity Adjustment and Multivariate Distributional Regression

arXiv 2005.14458 ↗First posted 29 May 2020stat.ML cs.LG stat.MEcited by 36

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Abstract (arXiv)

Random Forest (Breiman, 2001) is a successful and widely used regression and classification algorithm. Part of its appeal and reason for its versatility is its (implicit) construction of a kernel-type weighting function on training data, which can also be used for targets other than the original mean estimation. We propose a novel forest construction for multivariate responses based on their joint conditional distribution, independent of the estimation target and the data model. It uses a new splitting criterion based on the MMD distributional metric, which is suitable for detecting heterogeneity in multivariate distributions. The induced weights define an estimate of the full conditional distribution, which in turn can be used for arbitrary and potentially complicated targets of interest. The method is very versatile and convenient to use, as we illustrate on a wide range of examples. The code is available as Python and R packages drf.

Key facts. Cited by 36 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 May 2020.

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Citing papers per year: 2022: 5; 2023: 6; 2024: 8; 2025: 8; 2026: 9.

36 papers.

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  1. 2022Adversarial random forests for density estimation and generative modelingcited by 44
  2. 2022Doubly Robust Distributionally Robust Off-Policy Evaluation and Learningcited by 34
  3. 2023textttcausalAssembly: Generating Realistic Production Data for Benchmarking Causal Discoverycited by 18
  4. 2022Characterization and Greedy Learning of Gaussian Structural Causal Models under Unknown Interventionscited by 12
  5. 2024Regression Trees for Fast and Adaptive Prediction Intervalscited by 11
  6. 2023An Efficient Doubly-Robust Test for the Kernel Treatment Effectcited by 10
  7. 2022Aggregating distribution forecasts from deep ensemblescited by 5
  8. 2023Confidence and Uncertainty Assessment for Distributional Random Forestscited by 5
  9. 2024Treeffuser: Probabilistic Predictions via Conditional Diffusions with Gradient-Boosted Treescited by 5
  10. 2022Distributional regression and its evaluation with the CRPS: Bounds and convergence of the minimax riskcited by 3
  11. 2024Efficient and Sharp Off-Policy Evaluation in Robust Markov Decision Processescited by 3
  12. 2024Model Free Prediction with Uncertainty Assessmentcited by 3
24 more
  1. 2024Online Distributional Regressioncited by 3
  2. 2025Multivariate Latent Recalibration for Conditional Normalizing Flowscited by 3
  3. 2023MMD-based Variable Importance for Distributional Random Forestcited by 2
  4. 2026The Generalised Kernel Covariance Measurecited by 2
  5. 2025Multivariate Uncertainty Quantification with Tomographic Quantile Forestscited by 1
  6. 2023A new methodology to predict the oncotype scores based on clinico-pathological data with similar tumor profilescited by 0
  7. 2023Generalized Random Forests using Fixed-Point Treescited by 0
  8. 2024Simplifying Random Forests' Probabilistic Forecastscited by 0
  9. 2024Scoring rule nets: beyond mean target prediction in multivariate regressioncited by 0
  10. 2024Interaction Testing in Variation Analysiscited by 0
  11. 2025Risk Bounds For Distributional Regressioncited by 0
  12. 2025LILI clustering algorithm: Limit Inferior Leaf Interval Integrated into Causal Forest for Causal Interferencecited by 0
  13. 2025Generative Regression with IQ-BARTcited by 0
  14. 2025Transforming Conditional Density Estimation Into a Single Nonparametric Regression Taskcited by 0
  15. 2025Forests of Uncertaint(r)ees: Using tree-based ensembles to estimate probability distributions of future conflictcited by 0
  16. 2025Distributional Random Forests for Complex Survey Designscited by 0
  17. 2026Reliable Real-Time Value at Risk Estimation via Quantile Regression Forest with Conformal Calibrationcited by 0
  18. 2026Nonparametric Distribution Regression Re-calibrationcited by 0
  19. 2026Distributional Split Criteria for Random Forests: Extensions, Shrinkage, and the Robustness of Mean Splittingcited by 0
  20. 2026Breaking the Curse with BAND: Nonparametric Distribution Estimation in High Dimensionscited by 0
  21. 2026Conditioning Tree-Based Diffusions and Flows for Probabilistic Tabular Regressioncited by 0
  22. 2026Scaling-Score Conformal Prediction for Multi-Target Regressioncited by 0
  23. 2026Wasserstein Causal Forests for Distribution-Valued Outcomescited by 0
  24. 2026Generalized Engression Modelscited by 0