将多个复杂因果模型映射到统一粗粒度模型，保持因果关系不变。
Multi-Level Causal Embeddings
- 提出多层级因果嵌入框架，实现不同模型间的因果信息融合。
- 定义一致性准则，确保细粒度模型在粗粒度中因果关系不丢失。
- 适用于异构数据集合并与跨模型因果推断，适合系统建模研究者。
因果模型的抽象可实现模型粗化，同时保留因果关系。本文提出因果嵌入框架，将多个详细因果模型映射至更粗粒度模型的子系统中。该框架是抽象的推广，引入广义一致性概念。通过定义多分辨率边际问题，展示了因果嵌入在统计边际问题与因果边际问题中的重要性；进一步说明其在整合具有不同表征的模型数据集中的实际应用价值。
原文摘要 · Abstract (English)
Abstractions of causal models allow for the coarsening of models such that relations of cause and effect are preserved. Whereas abstractions focus on the relation between two models, in this paper we study a framework for causal embeddings which enable multiple detailed models to be mapped into sub-systems of a coarser causal model. We define causal embeddings as a generalization of abstraction, and present a generalized notion of consistency. By defining a multi-resolution marginal problem, we showcase the relevance of causal embeddings for both the statistical marginal problem and the causal marginal problem; furthermore, we illustrate its practical use in merging datasets coming from models with different representations.
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