Adversarial examples derived from deliberately crafted perturbations on
visual inputs can easily harm decision process of deep neural networks. To
prevent potential threats, various adversarial training-based defense methods
have grown rapidly and become a de facto standard approach for robustness.
Despite recent competitive achievements, we observe that adversarial
vulnerability varies across targets and certain vulnerabilities remain
prevalent. Intriguingly, such peculiar phenomenon cannot be relieved even with
deeper architectures and advanced defense methods. To address this issue, in
this paper, we introduce a causal approach called Adversarial Double Machine
Learning (ADML), which allows us to quantify the degree of adversarial
vulnerability for network predictions and capture the effect of treatments on
outcome of interests. ADML can directly estimate causal parameter of
adversarial perturbations per se and mitigate negative effects that can
potentially damage robustness, bridging a causal perspective into the
adversarial vulnerability. Through extensive experiments on various CNN and
Transformer architectures, we corroborate that ADML improves adversarial
robustness with large margins and relieve the empirical observation.

本文介绍了一种因果方法 ADML，通过量化网络预测的敏感程度和影响，直接估计对抗攻击的因果参数，并在 CNN 和 Transformer 等不同架构上进行广泛实验，证明了 ADML 的优越性和可行性。

通过对抗双机器学习进行因果参数估计来减轻对抗性漏洞

Mitigating Adversarial Vulnerability through Causal Parameter Estimation  by Adversarial Double Machine Learning

The chest X-ray (CXR) is one of the most common and easy-to-get medical tests
used to diagnose common diseases of the chest. Recently, many deep
learning-based methods have been proposed that are capable of effectively
classifying CXRs. Even though these techniques have worked quite well, it is
difficult to establish whether what these algorithms actually learn is the
cause-and-effect link between diseases and their causes or just how to map
labels to photos.In this paper, we propose a causal approach to address the CXR
classification problem, which constructs a structural causal model (SCM) and
uses the backdoor adjustment to select effective visual information for CXR
classification. Specially, we design different probability optimization
functions to eliminate the influence of confounders on the learning of real
causality. Experimental results demonstrate that our proposed method
outperforms the open-source NIH ChestX-ray14 in terms of classification
performance.

本文用因果学方法构建了结构性因果模型 (SCM)，利用反向门控调整选取肺部 X 光的有效视觉信息，去除混杂物对真实因果性学习的影响，实验结果表明，我们所提出的方法能够胜过开源 NIH ChestX-ray14 在分类性能方面。

胸部 X 射线图像分类：因果关系视角

Chest X-ray Image Classification: A Causal Perspective

The goal of recommendation is to show users items that they will like. Though
usually framed as a prediction, the spirit of recommendation is to answer an
interventional question---for each user and movie, what would the rating be if
we "forced" the user to watch the movie? To this end, we develop a causal
approach to recommendation, one where watching a movie is a "treatment" and a
user's rating is an "outcome." The problem is there may be unobserved
confounders, variables that affect both which movies the users watch and how
they rate them; unobserved confounders impede causal predictions with
observational data. To solve this problem, we develop the deconfounded
recommender, a way to use classical recommendation models for causal
recommendation. Following Wang & Blei [23], the deconfounded recommender
involves two probabilistic models. The first models which movies the users
watch; it provides a substitute for the unobserved confounders. The second one
models how each user rates each movie; it employs the substitute to help
account for confounders. This two-stage approach removes bias due to
confounding. It improves recommendation and enjoys stable performance against
interventions on test sets.

推荐系统通过因果分析的方法解决了推荐过程中潜在变量的干扰，引入去混淆因素的推荐模型成功地提高了推荐准确性。

消除混淆推荐器：推荐的因果推理方法

The Deconfounded Recommender: A Causal Inference Approach to  Recommendation

We consider the problem of learning fair decision systems in complex
scenarios in which a sensitive attribute might affect the decision along both
fair and unfair pathways. We introduce a causal approach to disregard effects
along unfair pathways that simplifies and generalizes previous literature. Our
method corrects observations adversely affected by the sensitive attribute, and
uses these to form a decision. This avoids disregarding fair information, and
does not require an often intractable computation of the path-specific effect.
We leverage recent developments in deep learning and approximate inference to
achieve a solution that is widely applicable to complex, non-linear scenarios.

在复杂情景中，针对敏感因素同时影响公平和不公平决策的情境下，本文提出了采用因果学方法来消除不公平路径效应的模型，并利用深度学习和近似推理实现了一个广泛适用于复杂非线性场景的解决方案。