Machine Intelligence for Individualized Decision Making Under a Counterfactual World: A Rejoinder
本文回应了在点识别、符号识别和部分识别下的个性化决策问题，通过下界视角统一了多种经典决策策略，并提出了针对机会主义者的最小最大遗憾规则。
This JASA rejoinder concerns the problem of individualized decision making under point, sign, and partial identification. The paper unifies various classical decision making strategies through a lower bound perspective proposed in Cui and Tchetgen Tchetgen (2020b) in the context of optimal treatment regimes under uncertainty due to unmeasured confounding. Building on this unified framework, the paper also provides a novel minimax solution (i.e., a rule that minimizes the maximum regret for so-called opportunists) for individualized decision making/policy assignment.