arXiv 15 Sep 2023 · Econometrics · publishedEconometric Reviews (2025) · 3 citations (OpenAlex)
arXiv:2309.08755 · PDF · DOI · OpenAlex · Extracted main text
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 paper 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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Breiman, Leo (2001) Random forests | 1.000 | 7 | 5 | 100% |
| 2 | Lechner, Michael, Okasa, Gabriel (2019) Random forest estimation of the ordered choice model | 1.000 | 7 | 5 | 100% |
| 3 | Wager, Stefan, Athey, Susan (2018) Estimation and inference of heterogeneous treatment effects using random forests | 1.000 | 7 | 3 | 100% |
| 4 | Athey, Susan, Tibshirani, Julie, Wager, Stefan (2019) Generalized random forests | 0.928 | 4 | 3 | 100% |
| 5 | Lechner, Michael, Mareckova, Jana (2022) Modified Causal Forest | 0.811 | 4 | 2 | 100% |
| 6 | Hornung, Roman (2020) Ordinal forests | 0.737 | 3 | 2 | 100% |
| 7 | Athey, Susan, Imbens, Guido W (2016) Recursive partitioning for heterogeneous causal effects | 0.644 | 2 | 2 | 100% |
| 8 | Efron, Bradley, Hastie, Trevor (2016) Computer Age Statistical Inference: Algorithms, Evidence, and Data Science | 0.644 | 2 | 2 | 100% |
| 9 | Hastie, T., Tibshirani, R., Friedman, J.H (2009) The Elements of Statistical Learning: Data Mining, Inference, and Prediction | 0.644 | 2 | 2 | 100% |
| 10 | Janitza, Silke, Tutz, Gerhard, Boulesteix, Anne-Laure (2016) Random forest for ordinal responses: prediction and variable selection | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 21 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Causal Inference for Qualitative Outcomes | 0.000 | 1 | 1 |