arXiv cs.AI· Winston Chou, M{\aa}rten Schultzberg, Lo-Hua Yuan, Wenjing Zheng, Avi Feller, David Holtz, Widad Machmouchi, Sven Schmit, Gagan Bhat, Simon Ejdemyr, Veronica Hannan, Amy Hartwig, Mahnaz Islam, Nathan Kallus, Jeremy Karp, Kyle Kretschman, Hannah Li, James McQueen, Evan Miller, Nicolas Stier, Sean Taylor·· 1 天前AI 评分29
从更多到更好：AI 时代的因果推断与数字实验
From More to Better: Causal Inference and Digital Experimentation in the Age of AI
AI 导读
基于 2026 年因果推断与数字实验圆桌（CIDER）的视角文章指出，AI 扩大组织数据生产与分析能力后，数据科学家的杠杆从执行单项分析转向评估发现的可靠性与重要性、判断证据能否跨情境泛化。文章从三方面展开：积累单次实验之外的学习价值、组合实验与观察及合成证据、让评估跟上 AI 能力与用法的演进，并区分成熟实践与开放问题。
正文 · 原文
Authors:Winston Chou, Mårten Schultzberg, Lo-Hua Yuan, Wenjing Zheng, Avi Feller, David Holtz, Widad Machmouchi, Sven Schmit, Gagan Bhat, Simon Ejdemyr, Veronica Hannan, Amy Hartwig, Mahnaz Islam, Nathan Kallus, Jeremy Karp, Kyle Kretschman, Hannah Li, James McQueen, Evan Miller, Nicolas Stier, Sean Taylor
Abstract:Artificial intelligence (AI) is quickly expanding the capacity of organizations to produce and analyze data. We argue that this expansion shifts the leverage of data scientists from performing individual analyses to assessing the credibility and importance of findings and determining when evidence generalizes across contexts. Drawing on a roundtable of academic researchers and industry practitioners, we examine three ways in which data scientists who work on causal inference and experimentation can help organizations turn more analytical capacity into better decisions: preserving and accumulating the learning value of experimentation beyond individual tests; choosing and combining experimental, observational, and synthetic evidence; and ensuring evaluation keeps pace with AI as its capabilities and uses evolve. In each area, we distinguish established practice from open questions. We conclude with implications for the training and practice of data science and for organizations.
| Comments: | 17 pages, 1 table. Perspective based on the 2026 Causal Inference and Digital Experimentation Roundtable (CIDER) |
| Subjects: | Artificial Intelligence (cs.AI); Applications (stat.AP) |
| Cite as: | arXiv:2611.16335 [cs.AI] |
| (or arXiv:2611.16335v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2611.16335 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Winston Chou [view email]
[v1]
Thu, 12 Nov 2026 05:56:52 UTC (18 KB)
来源：arXiv cs.AI · arxiv.org