将重叠数据集的因果图整合问题转化为高效求解的逻辑编程模型。
ION-C: Integration of Overlapping Networks via Constraints
- 用答案集编程（ASP）重构因果图整合算法，提升计算效率。
- 实验显示变量重叠程度对求解时间与结果一致性影响最大。
- 适用于多轮调查等存在部分观测重叠的真实因果推断场景。
在许多因果学习问题中，目标变量往往并非在同一组观测中测量，而是分布在多个具有重叠变量的数据集上。Tillman 等人（2008）首次提出 ION 算法，通过利用局部独立性关系枚举与所有输入图一致的最小等价类有向无环图（DAG）。本文将该问题重新表述为更高效的答案集编程（ASP）问题，命名为 ION-C，使用 ASP 求解器 clingo 进行求解。ION-C 在不同规模、密度及子图重叠度的随机合成图上进行了测试，结果显示重叠度对运行时间、解的数量及输出集合内的一致性影响最大。为验证其在真实数据上的表现，我们基于欧洲社会调查（ESS）两轮数据构建的重叠图运行了 ION-C，采用联合独立性检验程序以避免输入不一致。
原文摘要 · Abstract (English)
In many causal learning problems, variables of interest are often not all measured over the same observations, but are instead distributed across multiple datasets with overlapping variables. Tillman et al. (2008) presented the first algorithm for enumerating the minimal equivalence class of ground-truth DAGs consistent with all input graphs by exploiting local independence relations, called ION. In this paper, this problem is formulated as a more computationally efficient answer set programming (ASP) problem, which we call ION-C, and solved with the ASP system clingo. The ION-C algorithm was run on random synthetic graphs with varying sizes, densities, and degrees of overlap between subgraphs, with overlap having the largest impact on runtime, number of solution graphs, and agreement within the output set. To validate ION-C on real-world data, we ran the algorithm on overlapping graphs learned from data from two successive iterations of the European Social Survey (ESS), using a procedure for conducting joint independence tests to prevent inconsistencies in the input.
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