HyperCausalLP: Causal Link Prediction using Hyper-Relational Knowledge Graph

• URL: http://arxiv.org/abs/2410.14679v1
• Date: Thu, 12 Sep 2024 21:01:30 GMT
• Title: HyperCausalLP: Causal Link Prediction using Hyper-Relational Knowledge Graph
• Authors: Utkarshani Jaimini, Cory Henson, Amit Sheth,
• Abstract summary: Causal networks are often incomplete with missing causal links. In the causal link A causes B causes C, the influence of A to C is influenced by B which is known as a mediator. This paper presents HyperCausalLP, an approach designed to find missing causal links with the help of mediator links.
• Score: 0.0
• License:
• Abstract: Causal networks are often incomplete with missing causal links. This is due to various issues, such as missing observation data. Recent approaches to the issue of incomplete causal networks have used knowledge graph link prediction methods to find the missing links. In the causal link A causes B causes C, the influence of A to C is influenced by B which is known as a mediator. Existing approaches using knowledge graph link prediction do not consider these mediated causal links. This paper presents HyperCausalLP, an approach designed to find missing causal links within a causal network with the help of mediator links. The problem of missing links is formulated as a hyper-relational knowledge graph completion. The approach uses a knowledge graph link prediction model trained on a hyper-relational knowledge graph with the mediators. The approach is evaluated on a causal benchmark dataset, CLEVRER-Humans. Results show that the inclusion of knowledge about mediators in causal link prediction using hyper-relational knowledge graph improves the performance on an average by 5.94% mean reciprocal rank.

Related papers

• Promoting Fairness in Link Prediction with Graph Enhancement [6.477859104817626]
  Link prediction is a crucial task in network analysis, but it has been shown to be prone to biased predictions. We study the fair link prediction problem, which aims to ensure that the predicted link probability is independent of the sensitive attributes of the connected nodes. We propose FairLink, a method that learns a fairness-enhanced graph to bypass the need for debiasing during the link predictor's training.
  arXiv  Detail & Related papers  (2024-09-13T09:18:29Z)
• Influence of Backdoor Paths on Causal Link Prediction [0.0]
  CausalLPBack is a novel approach to causal link prediction that eliminates backdoor paths and uses knowledge graph link prediction methods. The evaluation involves a unique dataset splitting method called the Markov-based split that's relevant for causal link prediction.
  arXiv  Detail & Related papers  (2024-09-12T22:16:36Z)
• CausalLP: Learning causal relations with weighted knowledge graph link prediction [5.3454230926797734]
  CausalLP formulates the issue of incomplete causal networks as a knowledge graph completion problem. The use of knowledge graphs to represent causal relations enables the integration of external domain knowledge. Two primary tasks are supported by CausalLP: causal explanation and causal prediction.
  arXiv  Detail & Related papers  (2024-04-23T20:50:06Z)
• Causal Lifting and Link Prediction [10.336445584242933]
  We develop the first causal model capable of dealing with path dependencies in link prediction. We show how structural pairwise embeddings exhibit lower bias and correctly represent the task's causal structure. We validate our theoretical findings on three scenarios for causal link prediction tasks.
  arXiv  Detail & Related papers  (2023-02-02T16:25:16Z)
• Counterfactual Fairness with Partially Known Causal Graph [85.15766086381352]
  This paper proposes a general method to achieve the notion of counterfactual fairness when the true causal graph is unknown. We find that counterfactual fairness can be achieved as if the true causal graph were fully known, when specific background knowledge is provided.
  arXiv  Detail & Related papers  (2022-05-27T13:40:50Z)
• Hyper-relationship Learning Network for Scene Graph Generation [95.6796681398668]
  We propose a hyper-relationship learning network, termed HLN, for scene graph generation. We evaluate HLN on the most popular SGG dataset, i.e., the Visual Genome dataset. For example, the proposed HLN improves the recall per relationship from 11.3% to 13.1%, and maintains the recall per image from 19.8% to 34.9%.
  arXiv  Detail & Related papers  (2022-02-15T09:26:16Z)
• Deconfounded Training for Graph Neural Networks [98.06386851685645]
  We present a new paradigm of decon training (DTP) that better mitigates the confounding effect and latches on the critical information. Specifically, we adopt the attention modules to disentangle the critical subgraph and trivial subgraph. It allows GNNs to capture a more reliable subgraph whose relation with the label is robust across different distributions.
  arXiv  Detail & Related papers  (2021-12-30T15:22:35Z)
• Fuzzy Stochastic Timed Petri Nets for Causal properties representation [68.8204255655161]
  Causal relations are frequently represented by directed graphs, with nodes denoting causes and links denoting causal influence. Common methods used for graphically representing causal scenarios are neurons, truth tables, causal Bayesian networks, cognitive maps and Petri Nets. We will show that, even though the traditional models are able to represent separately some of the properties aforementioned, they fail trying to illustrate indistinctly all of them.
  arXiv  Detail & Related papers  (2020-11-24T13:22:34Z)
• Line Graph Neural Networks for Link Prediction [71.00689542259052]
  We consider the graph link prediction task, which is a classic graph analytical problem with many real-world applications. In this formalism, a link prediction problem is converted to a graph classification task. We propose to seek a radically different and novel path by making use of the line graphs in graph theory. In particular, each node in a line graph corresponds to a unique edge in the original graph. Therefore, link prediction problems in the original graph can be equivalently solved as a node classification problem in its corresponding line graph, instead of a graph classification task.
  arXiv  Detail & Related papers  (2020-10-20T05:54:31Z)
• Learning to Extrapolate Knowledge: Transductive Few-shot Out-of-Graph Link Prediction [69.1473775184952]
  We introduce a realistic problem of few-shot out-of-graph link prediction. We tackle this problem with a novel transductive meta-learning framework. We validate our model on multiple benchmark datasets for knowledge graph completion and drug-drug interaction prediction.
  arXiv  Detail & Related papers  (2020-06-11T17:42:46Z)
• Uncertainty Weighted Causal Graphs [0.0]
  Causality has traditionally been a scientific way to generate knowledge by relating causes to effects. We will attempt to go a step further modelling the uncertainty in the graph through probabilistic improving the management of the imprecision in the quoted graph.
  arXiv  Detail & Related papers  (2020-02-02T16:32:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.