Bayesian Additive Regression Trees
By Hugh A. Chipman, Edward I. George, Robert E. McCulloch. 2006
133 citations recorded by the source corpus.
Subfields: Optimization & Training, Probabilistic & Bayesian Methods
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Optimization & Training
- Ahmed M. Alaa
- Mihaela van der Schaar
- Francesca Dominici
- Susan Athey
- Stefan Wager
- Sudeepa Roy
- Aaron Fisher
- Cynthia Rudin
In Probabilistic & Bayesian Methods
- Ahmed M. Alaa
- Mihaela van der Schaar
- Francesca Dominici
- Susan Athey
- Stefan Wager
- Sudeepa Roy
- Aaron Fisher
- Cynthia Rudin
Citations in the corpus
- FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference (2017, methodology): “ith oracle variable selection, (3) Genetic Matching (GenMatch) (Diamond and Sekhon, 2013), (4) Causal Forest (Wager and Athey, 2018), (5) Mahalanobis Matching, (6) double linear regression, (7) BART (Chipman et al., 2010) and (8) CTMLE (Van Der Laan and Rubin, 2006). For 1-PSNNM with oracle variable”
- Estimation and Inference of Heterogeneous Treatment Effects using Random Forests (2015, methodology): “These papers use the Bayesian additive regression tree (BART) method of Chipman et al. [2010], and report posterior credible intervals obtained by Markov-chain Monte Carlo (MCMC) sampling based on a convenience prior.”
- Quantifying Uncertainty in Random Forests via Confidence Intervals and Hypothesis Tests (2014, unknown): “Chipman et al. (2010) have received significant attention for developing BART , a Bayesian “sum-of-trees” statistical model for the underlying regression function that allows for pointwise posterior inference throughout the feature space as well as estimates for individual feature effects.”
- All Models are Wrong but many are Useful: Variable Importance for Black-Box, Proprietary, or Misspecified Prediction Models, using Model Class Reliance (2018, methodology): “eural networks or random forests, approximate inference is sometimes achieved via approximate optimization procedures (for example, Markov chain Monte Carlo for Bayesian additive regression trees, in Chipman et al., 2010). Proposition 7 shows that similar approximate optimization methods could be re”
- Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processes (2017, background): “Contribution At the heart of this paper lies a novel conception of causal inference as a multi-task learning problem.”