通过基数修复检验因果主张
Stress-Testing Causal Claims via Cardinality Repairs
AI总结：
SubCure通过基数修复方法检验因果分析的稳健性，揭示数据中的潜在漏洞。
摘要：
基于观测数据的因果分析在医疗、公共政策和经济等领域支撑着高风险决策。然而，这些结论可能异常脆弱：即使是最小的数据错误——重复记录或输入错误——也可能大幅改变因果关系。这提出了一个根本问题：因果主张在数据的微小、有针对性的修改下有多稳健？回答这个问题对于确保经验发现的可靠性、可解释性和可重复性至关重要。我们介绍了SubCure，一种通过基数修复进行稳健性审计的框架。给定一个因果查询和用户指定的估计效应目标范围，SubCure识别出一小组元组或子群体，其移除会使估计值进入期望范围。这一过程不仅量化了因果结论的敏感性，还指出了驱动这些结论的数据特定区域。我们在此基础上在元组级和模式级删除设置下正式化了该问题，并证明两者均为NP完全问题。为了扩展到大规模数据集，我们开发了高效的算法，这些算法结合了机器无学习技术，以增量方式更新因果估计，而无需重新训练。我们在四个涵盖不同应用领域的现实数据集中评估了SubCure。在每种情况下，它都揭示了紧凑、高影响子集，其移除显著改变了因果结论，暴露了传统方法无法检测到的漏洞。我们的结果表明，基数修复是一种强大而通用的工具，用于压力测试因果分析，并防止根植于普通数据不完美中的误导性主张。
英文摘要：
Causal analyses derived from observational data underpin high-stakes decisions in domains such as healthcare, public policy, and economics. Yet such conclusions can be surprisingly fragile: even minor data errors - duplicate records, or entry mistakes - may drastically alter causal relationships. This raises a fundamental question: how robust is a causal claim to small, targeted modifications in the data? Addressing this question is essential for ensuring the reliability, interpretability, and reproducibility of empirical findings. We introduce SubCure, a framework for robustness auditing via cardinality repairs. Given a causal query and a user-specified target range for the estimated effect, SubCure identifies a small set of tuples or subpopulations whose removal shifts the estimate into the desired range. This process not only quantifies the sensitivity of causal conclusions but also pinpoints the specific regions of the data that drive those conclusions. We formalize this problem under both tuple- and pattern-level deletion settings and show both are NP-complete. To scale to large datasets, we develop efficient algorithms that incorporate machine unlearning techniques to incrementally update causal estimates without retraining from scratch. We evaluate SubCure across four real-world datasets covering diverse application domains. In each case, it uncovers compact, high-impact subsets whose removal significantly shifts the causal conclusions, revealing vulnerabilities that traditional methods fail to detect. Our results demonstrate that cardinality repair is a powerful and general-purpose tool for stress-testing causal analyses and guarding against misleading claims rooted in ordinary data imperfections.
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