mlsanityData to 7 Oct 2026

Discussion on "Bayesian Regression Tree Models for Causal Inference: Regularization, Confounding, and Heterogeneous Effects" by Hahn, Murray and Carvalho

arXiv 2108.02836 ↗First posted 5 Aug 2021stat.ME stat.OTcited by 77

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Abstract (arXiv)

Hahn et al. (2020) offers an extensive study to explicate and evaluate the performance of the BCF model in different settings and provides a detailed discussion about its utility in causal inference. It is a welcomed addition to the causal machine learning literature. I will emphasize the contribution of the BCF model to the field of causal inference through discussions on two topics: 1) the difference between the PS in the BCF model and the Bayesian PS in a Bayesian updating approach, 2) an alternative exposition of the role of the PS in outcome modeling based methods for the estimation of causal effects. I will conclude with comments on avenues for future research involving BCF that will be important and much needed in the era of Big data.

Key facts. Cited by 77 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Aug 2021.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 16; 2023: 16; 2024: 16; 2025: 14; 2026: 12.

77 papers.

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  1. 2025CausalPFN: Amortized Causal Effect Estimation via In-Context Learningcited by 43
  2. 2023In Search of Insights, Not Magic Bullets: Towards Demystification of the Model Selection Dilemma in Heterogeneous Treatment Effect Estimationcited by 29
  3. 2021SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Datacited by 25
  4. 2022Evaluation Methods and Measures for Causal Learning Algorithmscited by 23
  5. 2023Conformal Meta-learners for Predictive Inference of Individual Treatment Effectscited by 23
  6. 2022Robust and Agnostic Learning of Conditional Distributional Treatment Effectscited by 18
  7. 2022Comparison of meta-learners for estimating multi-valued treatment heterogeneous effectscited by 10
  8. 2022A Survey of Deep Causal Models and Their Industrial Applicationscited by 10
  9. 2023An Efficient Doubly-Robust Test for the Kernel Treatment Effectcited by 10
  10. 2025TabPFN: One Model to Rule Them All?cited by 10
  11. 2021Estimating Potential Outcome Distributions with Collaborating Causal Networkscited by 8
  12. 2022Generalized Bayesian Additive Regression Trees Models: Beyond Conditional Conjugacycited by 8
38 more
  1. 2021Identifiable Energy-based Representations: An Application to Estimating Heterogeneous Causal Effectscited by 7
  2. 2022Stochastic Tree Ensembles for Estimating Heterogeneous Effectscited by 7
  3. 2022A Mixing Time Lower Bound for a Simplified Version of BARTcited by 7
  4. 2023Proximal Causal Learning of Conditional Average Treatment Effectscited by 7
  5. 2022Covariate-Balancing-Aware Interpretable Deep Learning models for Treatment Effect Estimationcited by 5
  6. 2024Minimax Regret Estimation for Generalizing Heterogeneous Treatment Effects with Multisite Datacited by 5
  7. 2022Meta-Learners for Estimation of Causal Effects: Finite Sample Cross-Fit Performancecited by 4
  8. 2023Bayesian Safe Policy Learning with Chance Constrained Optimization: Application to Military Security Assessment during the Vietnam Warcited by 4
  9. 2023Uncertainty Quantification in Heterogeneous Treatment Effect Estimation with Gaussian-Process-Based Partially Linear Modelcited by 4
  10. 2025Hybrid Meta-learners for Estimating Heterogeneous Treatment Effectscited by 4
  11. 2022GCF: Generalized Causal Forest for Heterogeneous Treatment Effect Estimation in Online Marketplacecited by 3
  12. 2023Assisting Clinical Decisions for Scarcely Available Treatment via Disentangled Latent Representationcited by 3
  13. 2024Practical Marketplace Optimization at Uber Using Causally-Informed Machine Learningcited by 3
  14. 2022Hierarchical Embedded Bayesian Additive Regression Treescited by 2
  15. 2022Doubly Robust Kernel Statistics for Testing Distributional Treatment Effectscited by 2
  16. 2022Heterogeneous Synthetic Learner for Panel Datacited by 2
  17. 2023Fairness Implications of Heterogeneous Treatment Effect Estimation with Machine Learning Methods in Policy-makingcited by 2
  18. 2023Transparency challenges in policy evaluation with causal machine learning -- improving usability and accountabilitycited by 2
  19. 2023CATE Lasso: Conditional Average Treatment Effect Estimation with High-Dimensional Linear Regressioncited by 2
  20. 2024Unveiling the Potential of Robustness in Selecting Conditional Average Treatment Effect Estimatorscited by 2
  21. 2024DISCRET: Synthesizing Faithful Explanations For Treatment Effect Estimationcited by 2
  22. 2024Estimating Heterogeneous Treatment Effects by Combining Weak Instruments and Observational Datacited by 2
  23. 2024Is merging worth it? Securely evaluating the information gain for causal dataset acquisitioncited by 2
  24. 2024Interventional Processes for Causal Uncertainty Quantificationcited by 2
  25. 2025Frugal, Flexible, Faithful: Causal Data Simulation via Frengressioncited by 2
  26. 2025Causal-EPIG: A Prediction-Oriented Active Learning Framework for CATE Estimationcited by 2
  27. 2022Benign-Overfitting in Conditional Average Treatment Effect Prediction with Linear Regressioncited by 1
  28. 2022A Double Machine Learning Trend Model for Citizen Science Datacited by 1
  29. 2022SoftBart: Soft Bayesian Additive Regression Treescited by 1
  30. 2023Bayesian Causal Forests for Multivariate Outcomes: Application to Irish Data From an International Large Scale Education Assessmentcited by 1
  31. 2023Matched Machine Learning: A Generalized Framework for Treatment Effect Inference With Learned Metricscited by 1
  32. 2023Safe and Interpretable Estimation of Optimal Treatment Regimescited by 1
  33. 2023A Review and Roadmap of Deep Causal Model from Different Causal Structures and Representationscited by 1
  34. 2023Causal inference with Machine Learning-Based Covariate Representationcited by 1
  35. 2024A flexible Bayesian g-formula for causal survival analyses with time-dependent confoundingcited by 1
  36. 2024Deep Learning for Causal Inference: A Comparison of Architectures for Heterogeneous Treatment Effect Estimationcited by 1
  37. 2024Ranking Perspective for Tree-based Methods with Applications to Symbolic Feature Selectioncited by 1
  38. 2024Ablation Studies for Novel Treatment Effect Estimation Modelscited by 1

50 most cited of 77 All 77 (JSON)