# Optimal regimes for algorithm-assisted human decision-making

arXiv: https://arxiv.org/abs/2203.03020 · HTML: https://mlsanity.com/paper/2203.03020/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Mar 2022.

## Built on by (most cited first, 9 of 9)

- [Optimal Treatment Regimes for Proximal Causal Learning](https://mlsanity.com/paper/2212.09494/) (2022)
- [Blessing from Human-AI Interaction: Super Reinforcement Learning in Confounded Environments](https://mlsanity.com/paper/2209.15448/) (2022)
- [Evaluating and Correcting Performative Effects of Decision Support Systems via Causal Domain Shift](https://mlsanity.com/paper/2403.00886/) (2024)
- [Medical Knowledge Integration into Reinforcement Learning Algorithms for Dynamic Treatment Regimes](https://mlsanity.com/paper/2407.00364/) (2024)
- [Personalized Pricing with Invalid Instrumental Variables: Identification, Estimation, and Policy Learning](https://mlsanity.com/paper/2302.12670/) (2023)
- [Learning Robust Treatment Rules for Censored Data](https://mlsanity.com/paper/2408.09155/) (2024)
- [A Graphical Approach to State Variable Selection in Off-policy Learning](https://mlsanity.com/paper/2501.00854/) (2025)
- [Deep Causal Behavioral Policy Learning: Applications to Healthcare](https://mlsanity.com/paper/2503.03724/) (2025)
- [2-Step Agent: A Framework for the Interaction of a Decision Maker with AI Decision Support](https://mlsanity.com/paper/2602.21889/) (2026)

## Abstract (arXiv)

We consider optimal regimes for algorithm-assisted human decision-making. Such regimes are decision functions of measured pre-treatment variables and, by leveraging natural treatment values, enjoy a "superoptimality" property whereby they are guaranteed to outperform conventional optimal regimes. When there is unmeasured confounding, the benefit of using superoptimal regimes can be considerable. When there is no unmeasured confounding, superoptimal regimes are identical to conventional optimal regimes. Furthermore, identification of the expected outcome under superoptimal regimes in non-experimental studies requires the same assumptions as identification of value functions under conventional optimal regimes when the treatment is binary. To illustrate the utility of superoptimal regimes, we derive new identification and estimation results in a common instrumental variable setting. We use these derivations to analyze examples from the optimal regimes literature, including a case study of the effect of prompt intensive care treatment on survival.