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.

翻译：基于观测数据的因果分析支撑着医疗、公共政策和经济等高风险领域的决策。然而，这类结论可能出人意料地脆弱：即使是微小的数据错误——如重复记录或录入失误——也可能彻底改变因果关系。这引发了一个根本性问题：因果主张对数据中微小、有针对性的修改具有多大的稳健性？解决这一问题对于确保实证研究结果的可靠性、可解释性和可复现性至关重要。我们提出了SubCure框架，一种通过基数修复进行稳健性审计的方法。给定一个因果查询和用户指定的估计效应目标范围，SubCure能够识别出一小组元组或子群体，移除这些数据可将估计值调整至目标范围内。该过程不仅量化了因果结论的敏感性，还精确定位了驱动这些结论的具体数据区域。我们在元组级和模式级删除两种设定下形式化了该问题，并证明两者均为NP完全问题。为扩展到大规模数据集，我们开发了高效算法，结合机器遗忘技术以增量更新因果估计，无需从头重新训练。我们在涵盖多个应用领域的四个真实世界数据集上评估了SubCure。在每种情况下，它都能发现紧凑且高影响的子集，移除这些子集会显著改变因果结论，揭示传统方法无法检测的脆弱性。我们的结果表明，基数修复是一种强大且通用的工具，可用于压力测试因果分析，并防范源于常见数据缺陷的误导性主张。