Influence of Backdoor Paths on Causal Link Prediction

• URL: http://arxiv.org/abs/2410.14680v1
• Date: Thu, 12 Sep 2024 22:16:36 GMT
• Title: Influence of Backdoor Paths on Causal Link Prediction
• Authors: Utkarshani Jaimini, Cory Henson, Amit Sheth,
• Abstract summary: 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.
• Score: 0.0
• License:
• 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.

Related papers

• PageRank Bandits for Link Prediction [72.61386754332776]
  Link prediction is a critical problem in graph learning with broad applications such as recommender systems and knowledge graph completion. This paper reformulates link prediction as a sequential decision-making process, where each link prediction interaction occurs sequentially. We propose a novel fusion algorithm, PRB (PageRank Bandits), which is the first to combine contextual bandits with PageRank for collaborative exploitation and exploration.
  arXiv  Detail & Related papers  (2024-11-03T02:39:28Z)
• HyperCausalLP: Causal Link Prediction using Hyper-Relational Knowledge Graph [0.0]
  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.
  arXiv  Detail & Related papers  (2024-09-12T21:01:30Z)
• 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)
• Variational Disentangled Graph Auto-Encoders for Link Prediction [10.390861526194662]
  This paper proposes a novel framework with two variants, the disentangled graph auto-encoder (DGAE) and the variational disentangled graph auto-encoder (VDGAE) The proposed framework infers the latent factors that cause edges in the graph and disentangles the representation into multiple channels corresponding to unique latent factors.
  arXiv  Detail & Related papers  (2023-06-20T06:25:05Z)
• DOMINO: Visual Causal Reasoning with Time-Dependent Phenomena [59.291745595756346]
  We propose a set of visual analytics methods that allow humans to participate in the discovery of causal relations associated with windows of time delay. Specifically, we leverage a well-established method, logic-based causality, to enable analysts to test the significance of potential causes. Since an effect can be a cause of other effects, we allow users to aggregate different temporal cause-effect relations found with our method into a visual flow diagram.
  arXiv  Detail & Related papers  (2023-03-12T03:40:21Z)
• 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)
• Link-Backdoor: Backdoor Attack on Link Prediction via Node Injection [1.9109292348200242]
  Link prediction, inferring the undiscovered or potential links of the graph, is widely applied in the real-world. In this paper, we propose Link-Backdoor to reveal the training vulnerability of the existing link prediction methods.
  arXiv  Detail & Related papers  (2022-08-14T04:30:54Z)
• Deconfounding to Explanation Evaluation in Graph Neural Networks [136.73451468551656]
  We argue that a distribution shift exists between the full graph and the subgraph, causing the out-of-distribution problem. We propose Deconfounded Subgraph Evaluation (DSE) which assesses the causal effect of an explanatory subgraph on the model prediction.
  arXiv  Detail & Related papers  (2022-01-21T18:05:00Z)
• Adversarial Robustness through the Lens of Causality [105.51753064807014]
  adversarial vulnerability of deep neural networks has attracted significant attention in machine learning. We propose to incorporate causality into mitigating adversarial vulnerability. Our method can be seen as the first attempt to leverage causality for mitigating adversarial vulnerability.
  arXiv  Detail & Related papers  (2021-06-11T06:55:02Z)
• Causal Expectation-Maximisation [70.45873402967297]
  We show that causal inference is NP-hard even in models characterised by polytree-shaped graphs. We introduce the causal EM algorithm to reconstruct the uncertainty about the latent variables from data about categorical manifest variables. We argue that there appears to be an unnoticed limitation to the trending idea that counterfactual bounds can often be computed without knowledge of the structural equations.
  arXiv  Detail & Related papers  (2020-11-04T10:25:13Z)
• Latent Causal Invariant Model [128.7508609492542]
  Current supervised learning can learn spurious correlation during the data-fitting process. We propose a Latent Causal Invariance Model (LaCIM) which pursues causal prediction.
  arXiv  Detail & Related papers  (2020-11-04T10:00:27Z)

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.