Influence of Backdoor Paths on Causal Link Prediction

TL;DR

This paper introduces CausalLPBack, a novel method that improves causal link prediction in knowledge graphs by removing confounder effects via backdoor path adjustment, leading to more accurate causal inference.

Contribution

It presents a neuro-symbolic framework for eliminating backdoor paths in causal link prediction, extending traditional causal AI concepts to knowledge graph analysis.

Findings

Achieves at least 30% improvement in MRR

Achieves at least 16% improvement in Hits@K

Demonstrates effectiveness on a causal reasoning benchmark dataset

Abstract

The current method for predicting causal links in knowledge graphs uses weighted causal relations. For a given link between cause-effect entities, the presence of a confounder affects the causal link prediction, which can lead to spurious and inaccurate results. We aim to block these confounders using backdoor path adjustment. Backdoor paths are non-causal association flows that connect the \textit{cause-entity} to the \textit{effect-entity} through other variables. Removing these paths ensures a more accurate prediction of causal links. This paper proposes CausalLPBack, a novel approach to causal link prediction that eliminates backdoor paths and uses knowledge graph link prediction methods. It extends the representation of causality in a neuro-symbolic framework, enabling the adoption and use of traditional causal AI concepts and methods. We demonstrate our approach using a causal reasoning benchmark dataset of simulated videos. The evaluation involves a unique dataset splitting method called the Markov-based split that's relevant for causal link prediction. The evaluation of the proposed approach demonstrates atleast 30\% in MRR and 16\% in Hits@K inflated performance for causal link prediction that is due to the bias introduced by backdoor paths for both baseline and weighted causal relations.