Beating the Winner's Curse via Inference-Aware Policy Optimization
通过考虑推断的政策优化缓解赢家诅咒AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要研究基于个体协变量的处理政策学习，以及相对既有政策的效果评价与推断。
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通过考虑推断的政策优化缓解赢家诅咒
- 研究问题
- 基于丰富个体特征学习处理政策时，如何降低优化利用预测误差造成的赢家诅咒，使预计改善更可能通过后续政策评价？
- 方法与思路
- 作者同时优化估计目标值和改善通过显著性检验的机会，刻画两者的帕累托前沿，再用机器学习估计前沿并在测试集评价。
- 材料陈述的贡献
- 摘要提出将下游推断纳入政策优化的策略，给出目标权衡的数学刻画与算法，使决策者可选择权衡，并用模拟展示效果。
- 阅读关注点
- 应核对政策改善的目标定义、显著性检验及数据划分、前沿估计误差，以及选择权衡后测试集评价保持有效的具体条件。
- 阅读边界
- 所据为 arXiv 预印本，同行评审及期刊接收未核实；摘要未说明因果识别、检验有效性条件或模拟数值，不能保证所选政策实际更优。
原文摘要
There has been a surge of recent interest in automatically learning policies to target treatment decisions based on rich individual covariates. In addition, practitioners want confidence that the learned policy has better performance than the incumbent policy according to downstream policy evaluation. However, due to the winner's curse -- an issue where the policy optimization procedure exploits prediction errors rather than finding actual improvements -- predicted performance improvements are often not substantiated by downstream policy evaluation. To address this challenge, we propose a novel strategy called inference-aware policy optimization, which modifies policy optimization to account for how the policy will be evaluated downstream. Specifically, it optimizes not only for the estimated objective value, but also for the chances that the estimate of the policy's improvement passes a significance test during downstream policy evaluation. We mathematically characterize the Pareto frontier of policies according to the tradeoff of these two goals. Based on our characterization, we design a policy optimization algorithm that estimates the Pareto frontier using machine learning models; then, the decision-maker can select the policy that optimizes their desired tradeoff, after which policy evaluation can be performed on the test set as usual. Finally, we perform simulations to illustrate the effectiveness of our methodology.