# Estimation of Individualized Decision Rules Based on an Optimized Covariate-Dependent Equivalent of Random Outcomes

arXiv: https://arxiv.org/abs/1908.10742 · HTML: https://mlsanity.com/paper/1908.10742/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Aug 2019.

## Built on by (most cited first, 3 of 3)

- [RISE: Robust Individualized Decision Learning with Sensitive Variables](https://mlsanity.com/paper/2211.06569/) (2022)
- [Learning Robust Treatment Rules for Censored Data](https://mlsanity.com/paper/2408.09155/) (2024)
- [Rejoinder: Learning Optimal Distributionally Robust Individualized Treatment Rules](https://mlsanity.com/paper/2110.08936/) (2021)

## Abstract (arXiv)

Recent exploration of optimal individualized decision rules (IDRs) for patients in precision medicine has attracted a lot of attention due to the heterogeneous responses of patients to different treatments. In the existing literature of precision medicine, an optimal IDR is defined as a decision function mapping from the patients' covariate space into the treatment space that maximizes the expected outcome of each individual. Motivated by the concept of Optimized Certainty Equivalent (OCE) introduced originally in citeben1986expected that includes the popular conditional-value-of risk (CVaR) citerockafellar2000optimization, we propose a decision-rule based optimized covariates dependent equivalent (CDE) for individualized decision making problems. Our proposed IDR-CDE broadens the existing expected-mean outcome framework in precision medicine and enriches the previous concept of the OCE. Numerical experiments demonstrate that our overall approach outperforms existing methods in estimating optimal IDRs under heavy-tail distributions of the data.