因果量的 Epsilon-可识别性研究
Epsilon-Identifiability of Causal Quantities
AI 导读
一篇 arXiv 论文提出“epsilon-可识别性”概念，证明在无法完全识别因果量时，只要某些子群行为可被限定在足够窄的界限内，多个因果概率可实现近似识别。论文展示了不可识别的因果效应和反事实概率在此条件下可被 epsilon-识别，并将该方法应用于单元选择问题。
正文 · 原文
Abstract:Identifying the effects of causes and causes of effects is vital in virtually every scientific field. Often, however, the needed probabilities may not be fully identifiable from the available data sources. This paper shows how approximate identifiability is still possible for several probabilities of causation. We term this epsilon-identifiability and demonstrate its usefulness in cases where the behavior of certain subpopulations can be restricted within sufficiently narrow bounds. In particular, we show how unidentifiable causal effects and counterfactual probabilities can be epsilon-identified when such allowances are made. Often, these allowances are easily measured and reasonably assumed. Finally, epsilon-identifiability is applied to the unit selection problem.
| Subjects: | Artificial Intelligence (cs.AI); Statistics Theory (math.ST) |
| Cite as: | arXiv:2301.12022 [cs.AI] |
| (or arXiv:2301.12022v2 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2301.12022 arXiv-issued DOI via DataCite |
Submission history
From: Ang Li [view email]
[v1]
Fri, 27 Jan 2023 23:16:57 UTC (32 KB)
[v2]
Tue, 27 Oct 2026 03:24:17 UTC (38 KB)
来源：arXiv cs.AI · arxiv.org