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Estimating Treatment Effects with Causal Forests: An Application

arXiv 1902.07409 ↗First posted 20 Feb 2019stat.MEcited by 58

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Abstract (arXiv)

We apply causal forests to a dataset derived from the National Study of Learning Mindsets, and consider resulting practical and conceptual challenges. In particular, we discuss how causal forests use estimated propensity scores to be more robust to confounding, and how they handle data with clustered errors.

Key facts. Cited by 58 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Feb 2019.

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Citing papers per year: 2020: 1; 2021: 6; 2022: 9; 2023: 7; 2024: 9; 2025: 17; 2026: 9.

58 papers.

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  1. 2021Causal Effect Inference for Structured Treatmentscited by 38
  2. 2022Empirical Analysis of Model Selection for Heterogeneous Causal Effect Estimationcited by 25
  3. 2021DoWhy: Addressing Challenges in Expressing and Validating Causal Assumptionscited by 20
  4. 2023Causal isotonic calibration for heterogeneous treatment effectscited by 16
  5. 2024End-to-End Cost-Effective Incentive Recommendation under Budget Constraint with Uplift Modelingcited by 10
  6. 2022BITES: Balanced Individual Treatment Effect for Survival datacited by 7
  7. 2021Using Non-Linear Causal Models to Study Aerosol-Cloud Interactions in the Southeast Pacificcited by 6
  8. 2023Variable importance for causal forests: breaking down the heterogeneity of treatment effectscited by 6
  9. 2022Optimal Treatment Regimes for Proximal Causal Learningcited by 5
  10. 2021Doing Great at Estimating CATE? On the Neglected Assumptions in Benchmark Comparisons of Treatment Effect Estimatorscited by 4
  11. 2021Heterogeneous Treatment Effect Estimation using machine learning for Healthcare application: tutorial and benchmarkcited by 4
  12. 2022Learning Optimal Dynamic Treatment Regimes Using Causal Tree Methods in Medicinecited by 4
38 more
  1. 2022What Makes Forest-Based Heterogeneous Treatment Effect Estimators Work?cited by 4
  2. 2023Comparison of Methods that Combine Multiple Randomized Trials to Estimate Heterogeneous Treatment Effectscited by 4
  3. 2024Inferring Heterogeneous Treatment Effects of Crashes on Highway Traffic: A Doubly Robust Causal Machine Learning Approachcited by 4
  4. 2024Optimizing Multi-Scale Representations to Detect Effect Heterogeneity Using Earth Observation and Computer Vision: Applications to Two Anti-Poverty RCTscited by 4
  5. 2024Measuring Variable Importance in Heterogeneous Treatment Effects with Confidencecited by 3
  6. 2025Robust Uplift Modeling with Large-Scale Contexts for Real-time Marketingcited by 3
  7. 2025Causal Knowledge Transfer for Multi-Agent Reinforcement Learning in Dynamic Environmentscited by 3
  8. 2022How causal machine learning can leverage marketing strategies: Assessing and improving the performance of a coupon campaigncited by 2
  9. 2023A Unified Framework of Policy Learning for Contextual Bandit with Confounding Bias and Missing Observationscited by 2
  10. 2023Fairness Implications of Heterogeneous Treatment Effect Estimation with Machine Learning Methods in Policy-makingcited by 2
  11. 2023Transparency challenges in policy evaluation with causal machine learning -- improving usability and accountabilitycited by 2
  12. 2024DISCRET: Synthesizing Faithful Explanations For Treatment Effect Estimationcited by 2
  13. 2024CURLS: Causal Rule Learning for Subgroups with Significant Treatment Effectcited by 2
  14. 2024DeCaf: A Causal Decoupling Framework for OOD Generalization on Node Classificationcited by 2
  15. 2025Model Agnostic Differentially Private Causal Inferencecited by 2
  16. 2020Deep PQR: Solving Inverse Reinforcement Learning using Anchor Actionscited by 1
  17. 2022Moderately-Balanced Representation Learning for Treatment Effects with Orthogonality Informationcited by 1
  18. 2023Targeting relative risk heterogeneity with causal forestscited by 1
  19. 2025Individualised Treatment Effects Estimation with Composite Treatments and Composite Outcomescited by 1
  20. 2025Potential of large language model-powered nudges for promoting daily water and energy conservationcited by 1
  21. 2025Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modelingcited by 1
  22. 2025Causal Machine Learning for Surgical Interventionscited by 1
  23. 2025Overlap-Adaptive Regularization for Conditional Average Treatment Effect Estimationcited by 1
  24. 2025A Relative Error-Based Evaluation Framework of Heterogeneous Treatment Effect Estimatorscited by 1
  25. 2025Learning Subgroups with Maximum Treatment Effects without Causal Heuristicscited by 1
  26. 2026Observationally Informed Adaptive Causal Experimental Designcited by 1
  27. 2026ShiJianBench: From Dialogue to Decision for Long-Horizon Evaluation of Investment Advisorscited by 1
  28. 2021Bayesian Model Averaging for Data Driven Decision Making when Causality is Partially Knowncited by 0
  29. 2022A Causal Approach for Business Optimization: Application on an Online Marketplacecited by 0
  30. 2022Policy learning for many outcomes of interest: Combining optimal policy trees with multi-objective Bayesian optimisationcited by 0
  31. 2024Robust CATE Estimation Using Novel Ensemble Methodscited by 0
  32. 2024Heterogeneous Treatment Effect Estimation under Noncompliance in the Illinois Workplace Wellness Study with Bayesian Tree Ensemblescited by 0
  33. 2025Reconciling Predictive Multiplicity in Practicecited by 0
  34. 2025Generative Learner for Distributional Causal Effectscited by 0
  35. 2025Assessing Surrogate Heterogeneity in Real World Data Using Meta-Learnerscited by 0
  36. 2025Causal rule ensemble approach for multi-arm datacited by 0
  37. 2025M-learner:A Flexible And Powerful Framework To Study Heterogeneous Treatment Effect In Mediation Modelcited by 0
  38. 2025Revolutionizing Clinical Trials: A Manifesto for AI-Driven Transformationcited by 0

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