Causal lifting and link prediction
Causal lifting and link prediction
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DOI：
10.1098/rspa.2023.0121
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发表时间：
2023-02
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影响因子：
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通讯作者：
Leonardo Cotta;Beatrice Bevilacqua;Nesreen Ahmed;Bruno Ribeiro
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文献类型：
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作者：
Leonardo Cotta;Beatrice Bevilacqua;Nesreen Ahmed;Bruno Ribeiro
现有的因果关系模型的链接预测假设一组内在的节点因素，一个固有的特性定义在节点的诞生，管理因果关系的演变，在图中的链接。然而，在某些因果关系的任务中，链接的形成是路径依赖的：链接干预的结果取决于现有的链接。不幸的是，这些现有的因果关系的方法不是专为路径依赖的链路的形成，作为级联功能的依赖关系之间的链接（由路径依赖性）是不可识别的，或者需要一个不切实际的控制变量的数量。为了克服这一点，我们开发了第一个因果模型能够处理链接预测中的路径依赖。在这项工作中，我们引入了因果提升的概念，独立兴趣的因果模型中的不变性，在图形上，允许使用有限的干预数据识别因果链接预测查询。此外，我们展示了结构成对嵌入如何表现出较低的偏差，并正确地表示任务的因果结构，而不是现有的节点嵌入，例如图神经网络节点嵌入和矩阵分解。最后，我们验证了我们的理论研究结果的三种情况下的因果关系预测任务：知识库完成，协方差矩阵估计和消费者产品推荐。
Existing causal models for link prediction assume an underlying set of inherent node factors—an innate characteristic defined at the node’s birth—that governs the causal evolution of links in the graph. In some causal tasks, however, link formation is path-dependent: the outcome of link interventions depends on existing links. Unfortunately, these existing causal methods are not designed for path-dependent link formation, as the cascading functional dependencies between links (arising from path dependence) are either unidentifiable or require an impractical number of control variables. To overcome this, we develop the first causal model capable of dealing with path dependencies in link prediction. In this work, we introduce the concept of causal lifting, an invariance in causal models of independent interest that, on graphs, allows the identification of causal link prediction queries using limited interventional data. Further, we show how structural pairwise embeddings exhibit lower bias and correctly represent the task’s causal structure, as opposed to existing node embeddings, e.g. graph neural network node embeddings and matrix factorization. Finally, we validate our theoretical findings on three scenarios for causal link prediction tasks: knowledge base completion, covariance matrix estimation and consumer-product recommendations.