SSL Framework for Causal Inconsistency between Structures and Representations

• URL: http://arxiv.org/abs/2310.18634v2
• Date: Tue, 31 Dec 2024 08:55:29 GMT
• Title: SSL Framework for Causal Inconsistency between Structures and Representations
• Authors: Hang Chen, Xinyu Yang, Keqing Du, Wenya Wang,
• Abstract summary: Cross-pollination between causal discovery and deep learning has led to increasingly extensive interactions. Indefinite Data has conflicts between causal relationships expressed by the causal structure and causal representation generated by deep learning models. To alleviate causal inconsistency, we proposed a self-supervised learning framework based on intervention.
• Score: 31.895570222735955
• License:
• Abstract: The cross-pollination between causal discovery and deep learning has led to increasingly extensive interactions. It results in a large number of deep learning data types (such as images, text, etc.) extending into the field of causal discovery, and a multitude of deep learning tasks have begun to utilize causal discovery to explore the internal causal structure and causal representation of data. In this paper, we first identified that a complex data type, ``Indefinite Data", has conflicts between causal relationships expressed by the causal structure and causal representation generated by deep learning models, a phenomenon referred to as causal inconsistency. We thoroughly analyzed related work to explain why only Indefinite Data exhibits causal inconsistency while other data types do not. Furthermore, to alleviate causal inconsistency, we proposed a self-supervised learning (SSL) framework based on intervention, hoping to provide more causal information from different intervention views to promote consistency between structure and representation. Extensive experiments have shown that the SSL framework enhances causal consistency and can further improve causal structure and representation learning performance. Additionally, we extended the SSL framework to three different downstream tasks and LLM instructions. The quantitative results of these applications all reflect the performance improvement brought about by causal consistency.

Related papers

• Causal Modeling in Multi-Context Systems: Distinguishing Multiple Context-Specific Causal Graphs which Account for Observational Support [12.738813972869528]
  Causal structure learning with data from multiple contexts carries both opportunities and challenges. Here we study the impact of differing observational support between contexts on the identifiability of causal graphs. We propose a framework to model context-specific independence within structural causal models.
  arXiv  Detail & Related papers  (2024-10-27T10:34:58Z)
• From Pre-training Corpora to Large Language Models: What Factors Influence LLM Performance in Causal Discovery Tasks? [51.42906577386907]
  This study explores the factors influencing the performance of Large Language Models (LLMs) in causal discovery tasks. A higher frequency of causal mentions correlates with better model performance, suggesting that extensive exposure to causal information during training enhances the models' causal discovery capabilities.
  arXiv  Detail & Related papers  (2024-07-29T01:45:05Z)
• Cause and Effect: Can Large Language Models Truly Understand Causality? [1.2334534968968969]
  This research proposes a novel architecture called Context Aware Reasoning Enhancement with Counterfactual Analysis(CARE CA) framework. The proposed framework incorporates an explicit causal detection module with ConceptNet and counterfactual statements, as well as implicit causal detection through Large Language Models. The knowledge from ConceptNet enhances the performance of multiple causal reasoning tasks such as causal discovery, causal identification and counterfactual reasoning.
  arXiv  Detail & Related papers  (2024-02-28T08:02:14Z)
• Multi-modal Causal Structure Learning and Root Cause Analysis [67.67578590390907]
  We propose Mulan, a unified multi-modal causal structure learning method for root cause localization. We leverage a log-tailored language model to facilitate log representation learning, converting log sequences into time-series data. We also introduce a novel key performance indicator-aware attention mechanism for assessing modality reliability and co-learning a final causal graph.
  arXiv  Detail & Related papers  (2024-02-04T05:50:38Z)
• Inducing Causal Structure for Abstractive Text Summarization [76.1000380429553]
  We introduce a Structural Causal Model (SCM) to induce the underlying causal structure of the summarization data. We propose a Causality Inspired Sequence-to-Sequence model (CI-Seq2Seq) to learn the causal representations that can mimic the causal factors. Experimental results on two widely used text summarization datasets demonstrate the advantages of our approach.
  arXiv  Detail & Related papers  (2023-08-24T16:06:36Z)
• How to Enhance Causal Discrimination of Utterances: A Case on Affective Reasoning [22.11437627661179]
  We propose the incorporation of textiti.i.i.d. noise terms into the conversation process, thereby constructing a structural causal model (SCM) To facilitate the implementation of deep learning, we introduce the cogn frameworks to handle unstructured conversation data, and employ an autoencoder architecture to regard the unobservable noise as learnable "implicit causes"
  arXiv  Detail & Related papers  (2023-05-04T07:45:49Z)
• Uncovering Main Causalities for Long-tailed Information Extraction [14.39860866665021]
  Long-tailed distributions caused by the selection bias of a dataset may lead to incorrect correlations. This motivates us to propose counterfactual IE (CFIE), a novel framework that aims to uncover the main causalities behind data.
  arXiv  Detail & Related papers  (2021-09-11T08:08:24Z)
• Learning Neural Causal Models with Active Interventions [83.44636110899742]
  We introduce an active intervention-targeting mechanism which enables a quick identification of the underlying causal structure of the data-generating process. Our method significantly reduces the required number of interactions compared with random intervention targeting. We demonstrate superior performance on multiple benchmarks from simulated to real-world data.
  arXiv  Detail & Related papers  (2021-09-06T13:10:37Z)
• Everything Has a Cause: Leveraging Causal Inference in Legal Text Analysis [62.44432226563088]
  Causal inference is the process of capturing cause-effect relationship among variables. We propose a novel Graph-based Causal Inference framework, which builds causal graphs from fact descriptions without much human involvement. We observe that the causal knowledge contained in GCI can be effectively injected into powerful neural networks for better performance and interpretability.
  arXiv  Detail & Related papers  (2021-04-19T16:13:10Z)
• CausalVAE: Structured Causal Disentanglement in Variational Autoencoder [52.139696854386976]
  The framework of variational autoencoder (VAE) is commonly used to disentangle independent factors from observations. We propose a new VAE based framework named CausalVAE, which includes a Causal Layer to transform independent factors into causal endogenous ones. Results show that the causal representations learned by CausalVAE are semantically interpretable, and their causal relationship as a Directed Acyclic Graph (DAG) is identified with good accuracy.
  arXiv  Detail & Related papers  (2020-04-18T20:09:34Z)

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.