mlsanityData to 7 Oct 2026

Stochastic Tree Ensembles for Estimating Heterogeneous Effects

arXiv 2209.06998 ↗First posted 15 Sep 2022stat.ML cs.LGcited by 7

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Abstract (arXiv)

Determining subgroups that respond especially well (or poorly) to specific interventions (medical or policy) requires new supervised learning methods tailored specifically for causal inference. Bayesian Causal Forest (BCF) is a recent method that has been documented to perform well on data generating processes with strong confounding of the sort that is plausible in many applications. This paper develops a novel algorithm for fitting the BCF model, which is more efficient than the previously available Gibbs sampler. The new algorithm can be used to initialize independent chains of the existing Gibbs sampler leading to better posterior exploration and coverage of the associated interval estimates in simulation studies. The new algorithm is compared to related approaches via simulation studies as well as an empirical analysis.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 10 of its 28 references that are in the map. First posted on arXiv 15 Sep 2022.

Builds on

10 of 28 references.

References · 10

  1. 2015Estimation and Inference of Heterogeneous Treatment Effects using Random Forestscited by 597
  2. 2016Generalized Random Forestscited by 309
  3. 2017Automated versus do-it-yourself methods for causal inference: Lessons learned from a data analysis competitioncited by 106
  4. 2021Discussion on "Bayesian Regression Tree Models for Causal Inference: Regularization, Confounding, and Heterogeneous Effects" by Hahn, Murray and Carvalhocited by 77
  5. 2017Bayesian regression tree models for causal inference: regularization, confounding, and heterogeneous effectscited by 41
  6. 2019Atlantic Causal Inference Conference (ACIC) Data Analysis Challenge 2017cited by 23
  7. 2020Stochastic tree ensembles for regularized nonlinear regressioncited by 14
  8. 2022Local Gaussian process extrapolation for BART models with applications to causal inferencecited by 5
  9. 2018XBART: Accelerated Bayesian Additive Regression Treescited by 4
  10. 2020Semi-supervised learning and the question of true versus estimated propensity scorescited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 2; 2026: 2.

7 papers.

  1. 2024Is merging worth it? Securely evaluating the information gain for causal dataset acquisitioncited by 2
  2. 2025Causal-EPIG: A Prediction-Oriented Active Learning Framework for CATE Estimationcited by 2
  3. 2024Deep Learning for Causal Inference: A Comparison of Architectures for Heterogeneous Treatment Effect Estimationcited by 1
  4. 2026Observationally Informed Adaptive Causal Experimental Designcited by 1
  5. 2024Bayesian Causal Forests for Longitudinal Data: Assessing the Impact of Part-Time Work on Growth in High School Mathematics Achievementcited by 0
  6. 2025Forests for Differences: Robust Causal Inference Beyond Parametric DiDcited by 0
  7. 2026Graph-Split Bayesian Causal Forest for Spatial Heterogeneous Treatment Effect Estimationcited by 0