Current state-of-the-art 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, i.e., the outcome
of link interventions depends on existing links. For instance, in the
customer-product graph of an online retailer, the effect of an 85-inch TV ad
(treatment) likely depends on whether the costumer already has an 85-inch TV.
Unfortunately, existing causal methods are impractical in these scenarios. The
cascading functional dependencies between links (due to path dependence) are
either unidentifiable or require an impractical number of control variables. In
order to remedy this shortcoming, this work develops the first causal model
capable of dealing with path dependencies in link prediction. It introduces the
concept of causal lifting, an invariance in causal models that, when satisfied,
allows the identification of causal link prediction queries using limited
interventional data. On the estimation side, we show how structural pairwise
embeddings -- a type of symmetry-based joint representation of node pairs in a
graph -- exhibit lower bias and correctly represent the causal structure of the
task, as opposed to existing node embedding methods, e.g., GNNs and matrix
factorization. Finally, we validate our theoretical findings on four datasets
under three different scenarios for causal link prediction tasks: knowledge
base completion, covariance matrix estimation and consumer-product
recommendations.

本文介绍了第一个能处理路径依赖的因果模型，它引入了因果提升的概念，并且证明了基于结构对的节点对称联合表征方法，具有更低的偏差和更好的因果结构表示能力。