Ordered correlation forest
提出有序相关森林估计量，用于处理有序分类结果，无需假设误差分布，能处理非线性关系，预测性能优于其他森林估计量，并给出标准误和边际效应置信区间。
.Empirical studies in various social sciences often involve categorical outcomes with inherent ordering, such as self-evaluations of subjective well-being and self-assessments in health domains. While ordered choice models, such as the ordered logit and ordered probit, are popular tools for analyzing these outcomes, they may impose restrictive parametric and distributional assumptions. This article introduces a novel estimator, the ordered correlation forest, that can naturally handle non linearities in the data and does not assume a specific error term distribution. The proposed estimator modifies a standard random forest splitting criterion to build a collection of forests, each estimating the conditional probability of a single class. Under an “honesty” condition, predictions are consistent and asymptotically normal. The weights induced by each forest are used to obtain standard errors for the predicted probabilities and the covariates’ marginal effects. Evidence from synthetic data shows that the proposed estimator features a superior prediction performance than alternative forest-based estimators and demonstrates its ability to construct valid confidence intervals for the covariates’ marginal effects. Comparisons using various real-world data sets further highlight the advantages of forest-based estimators over parametric models in larger samples while showing that the ordered correlation forest remains competitive in smaller samples.