发表机构
Istituto Dalle Molle di Studi sull’Intelligenza Artificiale (IDSIA); Scuola Universitaria Professionale della Svizzera Italiana (SUPSI)（达勒·莫勒人工智能研究所; 瑞士意大利语区应用科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出利用反事实查询隐含的因果拓扑序，将反事实识别转化为线性规划，推广了Tian和Pearl的框架，证明了边界紧性，无需完整因果图即可界定反事实查询。
AI 中文摘要
反事实查询的非参数（部分）识别通常依赖完全指定的因果图。受领域知识不完整场景的启发，我们利用查询本身固有隐含的结构假设，对该要求提出挑战。我们表明，任何反事实查询都会对相关变量产生一个（通常是部分的）拓扑序，这进而能够进行显式的查询参数化，将识别任务简化为线性规划，从而可以对任意反事实和嵌套反事实查询进行边界界定。我们的工作可视为Tian和Pearl（2000）经典边界框架的推广，该框架最初用于因果概率研究。我们还通过构建既与观测数据兼容又与查询隐含序兼容的结构因果模型，证明了我们边界的紧性。为评估所提出边界过程的通用性和实用性，我们重新审视了文献中的几个案例研究，证明即使在没有输入因果图的情况下，推导的边界也能用于产生有价值的见解。
英文摘要
Non-parametric (partial) identification of counterfactual queries typically relies on a fully specified causal graph. Motivated by settings with incomplete domain knowledge, we challenge this requirement by leveraging structural assumptions that are inherently implied by the query itself. We show that any counterfactual inquiry induces a, mostly partial, topological ordering over relevant variables, which, in turn, enables an explicit query parametrisation reducing the identification task to a linear program. This allows bounding arbitrary counterfactual and nested counterfactual queries. Our work can be viewed as a generalisation of the classical bounding framework of Tian and Pearl (2000), originally developed for probabilities of causation. We also prove the \emph{tightness} of our bounds by constructing structural causal models that attain the bounds whilst being compatible with both the observed data and the query-implied order. To assess both the generality and practical utility of the proposed bounding procedure, we revisit several case studies from the literature, demonstrating how the derived bounds can be used to yield informative insights even in the absence of an input causal graph.
Comments15 pages, 3 figures, 3 tables, 3 appendices
