# Censored Quantile Regression Forest

arXiv: https://arxiv.org/abs/2001.03458 · HTML: https://mlsanity.com/paper/2001.03458/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jan 2020.

## Built on by (most cited first, 6 of 6)

- [Conformalized Survival Analysis](https://mlsanity.com/paper/2103.09763/) (2021)
- [T-SCI: A Two-Stage Conformal Inference Algorithm with Guaranteed Coverage for Cox-MLP](https://mlsanity.com/paper/2103.04556/) (2021)
- [Modeling Censored Mobility Demand through Quantile Regression Neural Networks](https://mlsanity.com/paper/2104.01214/) (2021)
- [Mind the Gap: Modelling Difference Between Censored and Uncensored Electric Vehicle Charging Demand](https://mlsanity.com/paper/2301.06418/) (2023)
- [Hybrid Censored Quantile Regression Forest to Assess the Heterogeneous Effects](https://mlsanity.com/paper/2212.05672/) (2022)
- [Global Censored Quantile Random Forest](https://mlsanity.com/paper/2410.12209/) (2024)

## Abstract (arXiv)

Random forests are powerful non-parametric regression method but are severely limited in their usage in the presence of randomly censored observations, and naively applied can exhibit poor predictive performance due to the incurred biases. Based on a local adaptive representation of random forests, we develop its regression adjustment for randomly censored regression quantile models. Regression adjustment is based on a new estimating equation that adapts to censoring and leads to quantile score whenever the data do not exhibit censoring. The proposed procedure named it censored quantile regression forest, allows us to estimate quantiles of time-to-event without any parametric modeling assumption. We establish its consistency under mild model specifications. Numerical studies showcase a clear advantage of the proposed procedure.