Causal Lifting and Link Prediction

by   Leonardo Cotta, et al.

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.


page 1

page 2

page 3

page 4


Counterfactual Graph Learning for Link Prediction

Learning to predict missing links is important for many graph-based appl...

Graph embeddings via matrix factorization for link prediction: smoothing or truncating negatives?

Link prediction – the process of uncovering missing links in a complex n...

ALPINE: Active Link Prediction using Network Embedding

Many real-world problems can be formalized as predicting links in a part...

Variational Disentangled Graph Auto-Encoders for Link Prediction

With the explosion of graph-structured data, link prediction has emerged...

Linkless Link Prediction via Relational Distillation

Graph Neural Networks (GNNs) have been widely used on graph data and hav...

A Hidden Challenge of Link Prediction: Which Pairs to Check?

The traditional setup of link prediction in networks assumes that a test...

Scalable Text and Link Analysis with Mixed-Topic Link Models

Many data sets contain rich information about objects, as well as pairwi...

Please sign up or login with your details

Forgot password? Click here to reset