A Review of Causal Feature Learning in Deep Learning Image Classification Models

WANG Xiaodong; JIANG Ling; LI Huihui; WANG Buhong

doi:10.11999/JEIT250738

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WANG Xiaodong, JIANG Ling, LI Huihui, WANG Buhong. A Review of Causal Feature Learning in Deep Learning Image Classification Models[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250738

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WANG Xiaodong, JIANG Ling, LI Huihui, WANG Buhong. A Review of Causal Feature Learning in Deep Learning Image Classification Models[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250738

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A Review of Causal Feature Learning in Deep Learning Image Classification Models

doi: 10.11999/JEIT250738 cstr: 32379.14.JEIT250738

1.
School of Information Science and Technology, Xiamen University Tan Kah Kee College, Zhangzhou 363105, China
2.
School of Electronic Science and Engineering, Xiamen University, Xiamen 361005, China
3.
College of Automation, Northwestern Polytechnical University, Xi’an 710129, China
4.
Information and Navigation School, Air Force Engineering University, Xi’an 710077, China
5.
Key Laboratory of Intelligent Manufacturing Equipment and Industrial Internet Technology, Fujian Provincial Universities, Zhangzhou 363105, China

Funds: The National Natural Science Foundation of China (62472437), The National Natural Science Foundation of Fujian (2023J01035), The Natural Science Foundation of Xiamen (3502Z20227326)

Received Date: 2025-08-07
Accepted Date: 2026-02-13
Rev Recd Date: 2026-02-12

Available Online: 2026-03-07

Abstract

Abstract

Significance The Deep Learning mechanism is constructed based on statistical correlations rather than causal relationships. Consequently, severe challenges in terms of generalization, interpretability, and stability are inevitably faced by such models. In contrast to human cognition, which mainly relies on causal discovery and exploitation, current Deep Learning models are still confined to the bottom of the "Pearl Causal Hierarchy (PCH)". Thus, the integration of causal inference into Deep Learning is highly anticipated. As the most crucial branch of Deep Learning, image classification models (represented by Convolutional Neural Networks, CNNs) exhibit particularly prominent shortcomings, and the introduction of causal inference is urgently required to address the bottleneck. Among various solutions for integrating causal inference into these models, Causal Feature Learning (CFL), a framework that combines unsupervised machine learning and causal inference, exhibits significant advantages. It is confirmed by studies that causal relationships are implicitly embedded in the pixel information of input image data for image classification tasks. According to the proven Causal Coarsening Theorem (CCT), causal knowledge can be acquired from observed image data at minimal experimental cost. In classification tasks, the optimal solution is constituted by the Markov Boundary (MB) of the causal Bayesian network for the class variable. The research endeavor to establish a connection between deep image classification models and causal inference via CFL is strongly supported by these theories. In general, the research significance of CFL has become increasingly prominent, and it is positioned as one of the potential breakthrough directions in the development of next-generation models. Progress This paper presents a comprehensive survey of CFL in Deep Learning image classification models from three core issues: statistical causal inference theory, correlation analysis methods and CFL implementations. First, the relevant definitions of CFL technology and its two mainstream statistical implementation frameworks, including causal discovery based on the Structural Causal Model (SCM) and causal effect estimation based on the Rubin Causal Model (RCM), are introduced. Second, correlation analysis methods for Deep Learning image classification models, which lie at the threshold of the PCH, are systematically summarized from three perspectives: forward, backward, and horizontal. Third, following the auxiliary tools, the progress of CFL for image classification is classified into four main aspects: Causal Feature Discovery (CFD), Causal Feature Effect Estimation (CFEE), Causal Representation Learning (CRL) and Spurious Correlation Removal (SCR). CFD is grounded in the SCM framework, aiming to derive confounding-free causal graphs through explicit or implicit causal intervention analyses on image data or models. Under the RCM framework, CFEE leverages observed image data to complete the quantitative evaluation of the causal effects of features, while overcoming the impacts of unknown counterfactual samples and confounding biases. CRL focuses on selecting or extracting high-dimensional features from image data to learn causal relationships and mine low-dimensional cross-image representations. SCR eliminates non-causal features from images and preserves causal ones via diverse methods. In addition , available toolkits, top conference resources and academic organizations are listed. Furthermore, this paper discusses key technical issues and future research directions. Conclusions This review summarizes the technological development of CFL. In general, considerable progress has been made, but difficulties in different research directions still need to be overcome. The advantages of CFD lie in that it is based on the basic logic of causal theory with clear and simple structures and is easy to accept. However, CFD suffers from immature processing methods for high-dimensional image data and insufficient generalization ability. CFEE can effectively distinguish causal features from confounding features. Its evaluation results are closer to real decision-making logic and show strong universality. Common problems of CFEE include the requirement for observable confounding factors, high dependence on causal assumptions, insufficient computational efficiency. CRL has the advantages of more optional dimensions and the ability to discover causal factors that drive classification and exclude non-causal factors. The core problems to be solved currently include generalization bias, factor coupling, prior dependence, weak evaluation, and high cost. SCR has strong pertinence but poor generalization. From a macro perspective, the implementation of CFL should not be limited to specific methods. All methods that aim to build causal relationships from micro-variables such as image pixels to causal macro-variables such as global semantics can be included, so it is an open research topic. Prospects The goal of causal inference is to go beyond correlation and clarify the causal relationships between variables by designing more rigorous experiments or employing advanced statistical methods. This requires deeper assumptions about feature relationships and more generalizable exploration of underlying causal chains, both of which are highly challenging and will become the main focus of future scholars in this field. To address the technical challenges in CFL, this paper proposes that future research can focus on the following directions: (1) Unifying the construction paradigms and establishing standards for image-based Structural Causal Models (SCMs), so as to improve the standardization and consistency of causal discovery; (2) Developing the RCM supported by generative artificial intelligence, to address the problem of sample scarcity in causal effect estimation; (3) Reforming models with the aim of learning novel image causal representations, thereby fundamentally resolving the inherent deficiencies of CNNs in CFL; (4) Integrating spurious correlation analysis with reinforcement learning, and leveraging reinforcement learning to endow Deep Learning image classification models with meta-learning capabilities for causal exploration. It can be asserted that, with the resolution of these key issues in CFL, there must be a qualitative improvement in accuracy, generalization, interpretability, and stability of Deep Learning images classification models.
- Deep learning,
- Images,
- Classification models,
- Causal inference,
- Causal feature learning

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References(128)

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