学习对未观测混杂因素鲁棒的风险评分
Learning Risk Scores Robust to Unobserved Confounders
- University of Southern California（南加州大学）
- University of Luxembourg（卢森堡大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对未观测混杂导致风险评分低估高风险个体的问题，提出结合敏感性分析与Wasserstein分布鲁棒优化的IPW改进方法，在半合成数据上校准性能提升达29.2%。
AI中文摘要：
我们考虑从受未观测混杂影响的历史观测数据中学习风险评分的问题，以优先分配稀缺资源或干预措施。关于谁获得稀缺资源的决策通常基于记录特征（如对调查的响应）得出的风险评分。这些风险评分越来越多地直接从观测数据中学习：即个体特征、分配决策和结果的历史记录。诸如逆倾向加权（IPW）之类的标准方法，用于纠正历史分配策略引入的偏差，如果历史决策过程完全由记录特征解释，则可用于学习准确的风险评分。然而，在实践中，历史决策通常依赖于未记录的信息，导致学习到的风险评分系统性地低估了那些其未记录情况推动了过往优先分配的个体。我们提出了一种基于IPW的学习风险评分方法，该方法对此类未观测混杂具有鲁棒性。由于在未观测混杂下无法可靠估计倾向权重，我们转而将其视为属于由可观测数据和领域对混杂程度的估计所确定的不确定性集合，将因果推断中的敏感性分析与Wasserstein分布鲁棒优化相结合。由此产生的鲁棒风险评分学习问题允许基于样本的近似，我们将其重新表述为与现成求解器兼容的指数锥规划。我们在源自UCI机器学习库数据集的半合成数据上展示了我们方法的有效性。我们的方法在不牺牲其他指标的情况下，将校准性能比传统基准提高了高达29.2%，比现有技术水平提高了高达11.1%。
英文摘要：
We consider the problem of learning risk scores to prioritize individuals for scarce resources or interventions, from historical observational data affected by unobserved confounding. Decisions about who receives scarce resources are often guided by risk scores based on recorded characteristics, such as responses to a survey. These risk scores are increasingly being learned directly from observational data: historical records of individuals' characteristics, allocation decisions, and outcomes. Standard methods such as inverse propensity weighting (IPW), which corrects for the bias introduced by the historical allocation policy, can be used to learn accurate risk scores if the historical decision process is fully explained by the recorded characteristics. In practice, however, historical decisions often depend on unrecorded information, causing learned risk scores to systematically under-prioritize exactly the individuals whose unrecorded circumstances drove past prioritization. We propose a method for learning risk scores that are robust to this kind of unobserved confounding, building on IPW. Since propensity weights cannot be reliably estimated under unobserved confounding, we instead treat them as belonging to an uncertainty set determined by the observable data and domain-informed estimates of the degree of confounding, combining sensitivity analysis from causal inference with Wasserstein distributionally robust optimization. The resulting robust risk score learning problem admits a sample-based approximation that we reformulate as an exponential cone program compatible with off-the-shelf solvers. We demonstrate the effectiveness of our approach on semi-synthetic data derived from datasets in the UCI Machine Learning Repository. Our method improves calibration by up to 29.2% over traditional benchmarks and up to 11.1% over the state of the art, without compromising other metrics.
