mlsanityData to 7 Oct 2026

Estimation and Inference of Heterogeneous Treatment Effects using Random Forests

arXiv 1510.04342 ↗First posted 14 Oct 2015stat.ME math.ST stat.MLcited by 597

Not on the map. Why?

Abstract (arXiv)

Many scientific and engineering challenges -- ranging from personalized medicine to customized marketing recommendations -- require an understanding of treatment effect heterogeneity. In this paper, we develop a non-parametric causal forest for estimating heterogeneous treatment effects that extends Breiman's widely used random forest algorithm. In the potential outcomes framework with unconfoundedness, we show that causal forests are pointwise consistent for the true treatment effect, and have an asymptotically Gaussian and centered sampling distribution. We also discuss a practical method for constructing asymptotic confidence intervals for the true treatment effect that are centered at the causal forest estimates. Our theoretical results rely on a generic Gaussian theory for a large family of random forest algorithms. To our knowledge, this is the first set of results that allows any type of random forest, including classification and regression forests, to be used for provably valid statistical inference. In experiments, we find causal forests to be substantially more powerful than classical methods based on nearest-neighbor matching, especially in the presence of irrelevant covariates.

Key facts. Cited by 597 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Oct 2015.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 89; 2022: 103; 2023: 100; 2024: 99; 2025: 111; 2026: 95.

597 papers.

Sort:

By area:

  1. 2021Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyondcited by 177
  2. 2021Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithmscited by 144
  3. 2022Causal Transformer for Estimating Counterfactual Outcomescited by 121
  4. 2025TabPFN-2.5: Advancing the State of the Art in Tabular Foundation Modelscited by 115
  5. 2022Deep End-to-end Causal Inferencecited by 93
  6. 2023A Survey of Contextual Optimization Methods for Decision Making under Uncertaintycited by 81
  7. 2021On Inductive Biases for Heterogeneous Treatment Effect Estimationcited by 74
  8. 2024Causal machine learning for predicting treatment outcomescited by 73
  9. 2021Learning from Counterfactual Links for Link Predictioncited by 69
  10. 2021VCNet and Functional Targeted Regularization For Learning Causal Effects of Continuous Treatmentscited by 59
  11. 2022Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equationscited by 59
  12. 2022Criteria for Classifying Forecasting Methodscited by 48
38 more
  1. 2025Do-PFN: In-Context Learning for Causal Effect Estimationcited by 46
  2. 2022Adversarial random forests for density estimation and generative modelingcited by 44
  3. 2021Graph Infomax Adversarial Learning for Treatment Effect Estimation with Networked Observational Datacited by 42
  4. 2021Conditional Distributional Treatment Effect with Kernel Conditional Mean Embeddings and U-Statistic Regressioncited by 40
  5. 2021Causal Effect Inference for Structured Treatmentscited by 38
  6. 2022Combining Observational and Randomized Data for Estimating Heterogeneous Treatment Effectscited by 32
  7. 2023Optimal Transport for Treatment Effect Estimationcited by 32
  8. 2024The Essential Role of Causality in Foundation World Models for Embodied AIcited by 32
  9. 2022Off-Policy Confidence Interval Estimation with Confounded Markov Decision Processcited by 31
  10. 2024Applied Causal Inference Powered by ML and AIcited by 30
  11. 2025Foundation Models for Causal Inference via Prior-Data Fitted Networkscited by 30
  12. 2021Evaluating Treatment Prioritization Rules via Rank-Weighted Average Treatment Effectscited by 29
  13. 2022Estimating average causal effects from patient trajectoriescited by 29
  14. 2023In Search of Insights, Not Magic Bullets: Towards Demystification of the Model Selection Dilemma in Heterogeneous Treatment Effect Estimationcited by 29
  15. 2021Large Scale Prediction with Decision Treescited by 28
  16. 2021Choice modelling in the age of machine learning -- discussion papercited by 26
  17. 2021Estimating Average Treatment Effects via Orthogonal Regularizationcited by 25
  18. 2021Causal Decision Making and Causal Effect Estimation Are Not the Same... and Why It Matterscited by 25
  19. 2021SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Datacited by 25
  20. 2022Exploring Transformer Backbones for Heterogeneous Treatment Effect Estimationcited by 25
  21. 2022Empirical Analysis of Model Selection for Heterogeneous Causal Effect Estimationcited by 25
  22. 2021Sequential Deconfounding for Causal Inference with Unobserved Confounderscited by 24
  23. 2021Federated Causal Inference in Heterogeneous Observational Datacited by 24
  24. 2023Sharp Bounds for Generalized Causal Sensitivity Analysiscited by 24
  25. 2022Evaluation Methods and Measures for Causal Learning Algorithmscited by 23
  26. 2023Conformal Meta-learners for Predictive Inference of Individual Treatment Effectscited by 23
  27. 2021SHAFF: Fast and consistent SHApley eFfect estimates via random Forestscited by 22
  28. 2022Normalizing Flows for Interventional Density Estimationcited by 21
  29. 2022Transfer Learning on Heterogeneous Feature Spaces for Treatment Effects Estimationcited by 21
  30. 2022Direct Heterogeneous Causal Learning for Resource Allocation Problems in Marketingcited by 21
  31. 2021Minimax Kernel Machine Learning for a Class of Doubly Robust Functionals with Application to Proximal Causal Inferencecited by 20
  32. 2021DoWhy: Addressing Challenges in Expressing and Validating Causal Assumptionscited by 20
  33. 2022Validating Causal Inference Methodscited by 20
  34. 2023Bounds on Representation-Induced Confounding Bias for Treatment Effect Estimationcited by 20
  35. 2022Robust and Agnostic Learning of Conditional Distributional Treatment Effectscited by 18
  36. 2024Model-agnostic meta-learners for estimating heterogeneous treatment effects over timecited by 18
  37. 2022LBCF: A Large-Scale Budget-Constrained Causal Forest Algorithmcited by 17
  38. 2022Estimating individual treatment effects under unobserved confounding using binary instrumentscited by 17

50 most cited of 597 All 597 (JSON)