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.
BooST: Boosting Smooth Trees for Partial Effect Estimation in Nonlinear Regressions
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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.
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econ.EM 1years
2019 1verdicts
CONDITIONAL 1representative citing papers
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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.