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ACM Manag. Data"],"published-print":{"date-parts":[[2026,4,2]]},"abstract":"<jats:p>\n                    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:\n                    <jats:italic toggle=\"yes\">how robust is a causal claim to small, targeted modifications in the data?<\/jats:italic>\n                    Addressing this question is essential for ensuring the reliability, interpretability, and reproducibility of empirical findings.\n                  <\/jats:p>\n                  <jats:p>\n                    We introduce\n                    <jats:sc>SubCure<\/jats:sc>\n                    , a framework for\n                    <jats:italic toggle=\"yes\">robustness auditing via cardinality repairs<\/jats:italic>\n                    . 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.\n                  <\/jats:p>\n                  <jats:p>\n                    We evaluate\n                    <jats:sc>SubCure<\/jats:sc>\n                    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\u2014revealing 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.\n                  <\/jats:p>","DOI":"10.1145\/3786700","type":"journal-article","created":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T17:54:13Z","timestamp":1775584453000},"page":"1-24","source":"Crossref","is-referenced-by-count":1,"title":["Stress-Testing Causal Claims via Cardinality Repairs"],"prefix":"10.1145","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-4330-8990","authenticated-orcid":false,"given":"Yarden","family":"Gabbay","sequence":"first","affiliation":[{"name":"Technion, Hifa, Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-5412-4489","authenticated-orcid":false,"given":"Haoquan","family":"Guan","sequence":"additional","affiliation":[{"name":"University of California, San Diego, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9021-1175","authenticated-orcid":false,"given":"Shaull","family":"Almagor","sequence":"additional","affiliation":[{"name":"Technion, Haifa, Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5187-3499","authenticated-orcid":false,"given":"El Kindi","family":"Rezig","sequence":"additional","affiliation":[{"name":"University of Utah, Salt lake city, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0031-5550","authenticated-orcid":false,"given":"Brit","family":"Youngmann","sequence":"additional","affiliation":[{"name":"Technion, Haifa, Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8763-8354","authenticated-orcid":false,"given":"Babak","family":"Salimi","sequence":"additional","affiliation":[{"name":"University of California, San Diego, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,4,7]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"2021. 2021 Stackoverflow Developer Survey. https:\/\/insights.stackoverflow.com\/survey\/2021."},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/1514894.1514899"},{"key":"e_1_2_1_3_1","volume-title":"Causal inference with corrupted data: Measurement error, missing values, discretization, and differential privacy. arXiv preprint arXiv:2107.02780","author":"Agarwal Anish","year":"2021","unstructured":"Anish Agarwal and Rahul Singh. 2021. 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