# Individualized Decision Making Under Partial Identification: ThreePerspectives, Two Optimality Results, and One Paradox

@article{Cui2021IndividualizedDM, title={Individualized Decision Making Under Partial Identification: ThreePerspectives, Two Optimality Results, and One Paradox}, author={Yifan Cui}, journal={Harvard Data Science Review}, year={2021} }

Unmeasured confounding is a threat to causal inference and gives rise to bi-ased estimates. In this paper, we consider the problem of individualized decision making under partial identiﬁcation. Firstly, we argue that when faced with unmeasured con-founding, one should pursue individualized decision making using partial identiﬁcation in a comprehensive manner. We establish a formal link between individualized decision making under partial identiﬁcation and classical decision theory by…

## 2 Citations

Machine Intelligence for Individualized Decision Making Under a Counterfactual World: A Rejoinder

- MedicineJournal of the American Statistical Association
- 2021

This JASA rejoinder provides a novel minimax solution (i.e., a rule that minimizes the maximum regret for so-called opportunists) for individualized decision making/policy assignment.

Safe Policy Learning through Extrapolation: Application to Pre-trial Risk Assessment

- Computer Science, MathematicsArXiv
- 2021

A robust optimization approach is developed that partially identifies the expected utility of a policy, and then finds an optimal policy by minimizing the worst-case regret, allowing the policy-maker to limit the probability of producing a worse outcome than the existing policy.

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