mlsanityData to 7 Oct 2026

Bayesian regression tree models for causal inference: regularization, confounding, and heterogeneous effects

arXiv 1706.09523 ↗First posted 29 Jun 2017stat.MEcited by 41

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Abstract (arXiv)

This paper presents a novel nonlinear regression model for estimating heterogeneous treatment effects from observational data, geared specifically towards situations with small effect sizes, heterogeneous effects, and strong confounding. Standard nonlinear regression models, which may work quite well for prediction, have two notable weaknesses when used to estimate heterogeneous treatment effects. First, they can yield badly biased estimates of treatment effects when fit to data with strong confounding. The Bayesian causal forest model presented in this paper avoids this problem by directly incorporating an estimate of the propensity function in the specification of the response model, implicitly inducing a covariate-dependent prior on the regression function. Second, standard approaches to response surface modeling do not provide adequate control over the strength of regularization over effect heterogeneity. The Bayesian causal forest model permits treatment effect heterogeneity to be regularized separately from the prognostic effect of control variables, making it possible to informatively "shrink to homogeneity". We illustrate these benefits via the reanalysis of an observational study assessing the causal effects of smoking on medical expenditures as well as extensive simulation studies.

Key facts. Cited by 41 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Jun 2017.

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Citing papers per year: 2021: 7; 2022: 11; 2023: 6; 2024: 5; 2025: 5; 2026: 7.

41 papers.

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  1. 2025Foundation Models for Causal Inference via Prior-Data Fitted Networkscited by 30
  2. 2022Robust and Agnostic Learning of Conditional Distributional Treatment Effectscited by 18
  3. 2021A Tree-based Model Averaging Approach for Personalized Treatment Effect Estimation from Heterogeneous Data Sourcescited by 14
  4. 2021Safe Policy Learning through Extrapolation: Application to Pre-trial Risk Assessmentcited by 10
  5. 2022Generalized Bayesian Additive Regression Trees Models: Beyond Conditional Conjugacycited by 8
  6. 2021Shrinkage Bayesian Causal Forests for Heterogeneous Treatment Effects Estimationcited by 7
  7. 2022Statistical Inference for Heterogeneous Treatment Effects Discovered by Generic Machine Learning in Randomized Experimentscited by 7
  8. 2022Feature Selection for Discovering Distributional Treatment Effect Modifierscited by 7
  9. 2022Stochastic Tree Ensembles for Estimating Heterogeneous Effectscited by 7
  10. 2022Local Gaussian process extrapolation for BART models with applications to causal inferencecited by 5
  11. 2022Meta-Learners for Estimation of Causal Effects: Finite Sample Cross-Fit Performancecited by 4
  12. 2022Double Robust Bayesian Inference on Average Treatment Effectscited by 4
29 more
  1. 2023Variable Importance Matching for Causal Inferencecited by 4
  2. 2023Causal Q-Aggregation for CATE Model Selectioncited by 4
  3. 2023The Connection Between R-Learning and Inverse-Variance Weighting for Estimation of Heterogeneous Treatment Effectscited by 3
  4. 2024Generalization Bounds for Causal Regression: Insights, Guarantees and Sensitivity Analysiscited by 3
  5. 2024Improve ROI with Causal Learning and Conformal Predictioncited by 3
  6. 2021Causal Inference with the Instrumental Variable Approach and Bayesian Nonparametric Machine Learningcited by 2
  7. 2021Accounting for shared covariates in semi-parametric Bayesian additive regression treescited by 2
  8. 2022Distributionally Robust Causal Inference with Observational Datacited by 2
  9. 2024Is merging worth it? Securely evaluating the information gain for causal dataset acquisitioncited by 2
  10. 2022Interpretable Deep Causal Learning for Moderation Effectscited by 1
  11. 2025Treatment response as a latent variablecited by 1
  12. 2025A Bayesian Additive Regression Tree Model for Learning Conditional Average Treatment Effects in Regression Discontinuity Designscited by 1
  13. 2025Horseshoe Forests for High-Dimensional Causal Survival Analysiscited by 1
  14. 2026Improving RCT-Based Treatment Effect Estimation Under Covariate Mismatch via Calibrated Alignmentcited by 1
  15. 2026Tailoring Strictly Proper Scoring Rules for Downstream Tasks: An Application to Causal Inferencecited by 1
  16. 2021Dive into Decision Trees and Forests: A Theoretical Demonstrationcited by 0
  17. 2021Deep Bayesian Estimation for Dynamic Treatment Regimes with a Long Follow-up Timecited by 0
  18. 2022Counterfactual Learning with Multioutput Deep Kernelscited by 0
  19. 2023Generalized Bayesian Additive Regression Trees: Theory and Softwarecited by 0
  20. 2023CIMLA: Interpretable AI for inference of differential causal networkscited by 0
  21. 2023Statistical Performance Guarantee for Subgroup Identification with Generic Machine Learningcited by 0
  22. 2024Neyman Meets Causal Machine Learning: Experimental Evaluation of Individualized Treatment Rulescited by 0
  23. 2024Oblique Bayesian additive regression treescited by 0
  24. 2025Incorporating structural uncertainty in causal decision makingcited by 0
  25. 2026Bayesian X-Learner: Calibrated Posterior Inference for Heterogeneous Treatment Effects under Heavy-Tailed Outcomescited by 0
  26. 2026BuddyBench: A Privacy-Constrained Multi-Task Benchmark for Pediatric Social-Communication Personalizationcited by 0
  27. 2026Bayesian Mediation Analysis for Individualized Treatment Rulescited by 0
  28. 2026Bayesian fusion forests for heterogeneous treatment effects on survival from randomised and real-world datacited by 0
  29. 2026Wasserstein Causal Forests for Distribution-Valued Outcomescited by 0