Deep learning has revolutionized the field of artificial intelligence. Based
on the statistical correlations uncovered by deep learning-based methods,
computer vision technology has contributed to tremendous growth in areas such
as autonomous driving and robotics. Despite being the basis of deep learning,
such correlation is not stable and is susceptible to uncontrolled factors. In
the absence of the guidance of prior knowledge, statistical correlations can
easily turn into spurious correlations and cause confounders. As a result,
researchers are beginning to refine deep learning-based methods with causal
theory. Causal theory models the intrinsic causal structure unaffected by data
bias and is effective in avoiding spurious correlations. This paper aims to
comprehensively review the existing causal methods in typical vision and
vision-language tasks such as semantic segmentation, object detection, and
image captioning. The advantages of causality and the approaches for building
causal paradigms will be summarized. Future roadmaps are also proposed,
including facilitating the development of causal theory and its application in
other complex scenes and systems.

基于深度学习的方法已经颠覆了人工智能领域。本文综述了在典型的计算机视觉和视觉语言任务中，深度学习的基础、稳定性以及存在的问题，并总结了因果理论对解决这些问题的优势以及构建因果模型的方法。未来的规划包括促进因果理论的发展并在其他复杂场景和系统中应用。

典型计算机视觉任务中的因果推理

Causal reasoning in typical computer vision tasks

Graph neural networks have shown great ability in representation (GNNs)
learning on graphs, facilitating various tasks. Despite their great performance
in modeling graphs, recent works show that GNNs tend to inherit and amplify the
bias from training data, causing concerns of the adoption of GNNs in high-stake
scenarios. Hence, many efforts have been taken for fairness-aware GNNs.
However, most existing fair GNNs learn fair node representations by adopting
statistical fairness notions, which may fail to alleviate bias in the presence
of statistical anomalies. Motivated by causal theory, there are several
attempts utilizing graph counterfactual fairness to mitigate root causes of
unfairness. However, these methods suffer from non-realistic counterfactuals
obtained by perturbation or generation. In this paper, we take a causal view on
fair graph learning problem. Guided by the casual analysis, we propose a novel
framework CAF, which can select counterfactuals from training data to avoid
non-realistic counterfactuals and adopt selected counterfactuals to learn fair
node representations for node classification task. Extensive experiments on
synthetic and real-world datasets show the effectiveness of CAF.

本研究提出了一种基于因果理论的框架，即 CAF，用于解决图学习中的公平性问题，通过选择训练数据中的反事实因素来学习公平的节点表示，以避免非现实反事实因素，并在合成和真实数据集上进行了广泛的实验验证其有效性。

基于图反事实方法的图神经网络公平性改进

Improving Fairness of Graph Neural Networks: A Graph Counterfactual  Perspective

Learning transferable knowledge across similar but different settings is a
fundamental component of generalized intelligence. In this paper, we approach
the transfer learning challenge from a causal theory perspective. Our agent is
endowed with two basic yet general theories for transfer learning: (i) a task
shares a common abstract structure that is invariant across domains, and (ii)
the behavior of specific features of the environment remain constant across
domains. We adopt a Bayesian perspective of causal theory induction and use
these theories to transfer knowledge between environments. Given these general
theories, the goal is to train an agent by interactively exploring the problem
space to (i) discover, form, and transfer useful abstract and structural
knowledge, and (ii) induce useful knowledge from the instance-level attributes
observed in the environment. A hierarchy of Bayesian structures is used to
model abstract-level structural causal knowledge, and an instance-level
associative learning scheme learns which specific objects can be used to induce
state changes through interaction. This model-learning scheme is then
integrated with a model-based planner to achieve a task in the OpenLock
environment, a virtual ``escape room'' with a complex hierarchy that requires
agents to reason about an abstract, generalized causal structure. We compare
performances against a set of predominate model-free reinforcement learning(RL)
algorithms. RL agents showed poor ability transferring learned knowledge across
different trials. Whereas the proposed model revealed similar performance
trends as human learners, and more importantly, demonstrated transfer behavior
across trials and learning situations.

采用因果理论的角度来解决迁移学习的问题，在 OpenLock 环境中实现了基于模型和 Bayesian 结构的计划者与模型学习方案，与强化学习相比，该模型表现出更好的迁移表现和类似于人类学习者的表现趋势。