REVIEW 1 cited by
BooST: Boosting Smooth Trees for Partial Effect Estimation in Nonlinear Regressions
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
In this paper, we introduce a new machine learning (ML) model for nonlinear regression called the Boosted Smooth Transition Regression Trees (BooST), which is a combination of boosting algorithms with smooth transition regression trees. The main advantage of the BooST model is the estimation of the derivatives (partial effects) of very general nonlinear models. Therefore, the model can provide more interpretation about the mapping between the covariates and the dependent variable than other tree-based models, such as Random Forests. We present several examples with both simulated and real data.
Forward citations
Cited by 1 Pith paper
-
Forward-Selected Panel Data Approach for Program Evaluation
Forward selection of control units in the panel data approach yields valid normal inference for average treatment effects even when the number of controls grows much faster than the time dimension and the true model is dense.
Discussion (0). Continue with ORCID to comment.