有因果关系的机器学习框架可以通过回答反事实问题来帮助临床医生确定最佳治疗方法。我们通过研究左心室射血分数的变化来探索超声心动图的情况,这是从这些检查中获得的最重要的临床指标。我们首次结合了深层神经网络,双因果网络和生成的对抗方法,以建立一种新颖的因果生成模型,这是建立D'Artagnan(深人造双胞胎生成网络)。在将其应用于心脏超声视频之前,我们在合成数据集上证明了我们的方法的合理性,以回答以下问题:“如果患者的射血分数不同,则超声心动图会怎样?”。为此,我们生成了新的超声视频,保留了原始患者的视频样式和解剖学,同时修改了以给定输入为条件的射血分数。我们在反事实视频中获得0.79的SSIM分数为0.79,R2得分为0.51。代码和型号可在以下网址提供:https://github.com/hreynaud/dartagnan。
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反事实推断是一种强大的工具,能够解决备受瞩目的领域中具有挑战性的问题。要进行反事实推断,需要了解潜在的因果机制。但是,仅凭观察和干预措施就不能独特地确定因果机制。这就提出了一个问题,即如何选择因果机制,以便在给定领域中值得信赖。在具有二进制变量的因果模型中已经解决了这个问题,但是分类变量的情况仍未得到解答。我们通过为具有分类变量的因果模型引入反事实排序的概念来应对这一挑战。为了学习满足这些约束的因果机制,并对它们进行反事实推断,我们引入了深层双胞胎网络。这些是深层神经网络,在受过训练的情况下,可以进行双网络反事实推断 - 一种替代绑架,动作和预测方法的替代方法。我们从经验上测试了来自医学,流行病学和金融的多种现实世界和半合成数据的方法,并报告了反事实概率的准确估算,同时证明了反事实订购时不执行反事实的问题。
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医学图像分析是一个充满活力的研究领域,为医生和医生提供了宝贵的见解以及准确诊断和监测疾病的能力。机器学习为该领域提供了额外的提升。但是,用于医学图像分析的机器学习尤其容易受到自然偏见的影响,例如影响算法性能和鲁棒性的域移位。在本文中,我们在技术准备水平的框架内分析了机器学习,以进行医学图像分析,并回顾因果分析方法在创建健壮且适应性的医学图像分析算法时如何填补空白。我们在医学成像AI/ML中使用因果关系回顾方法,发现因果分析有可能减轻临床翻译的关键问题,但是到目前为止,摄取和临床下游研究受到限制。
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改变特定特征但不是其他特性的输入扰动的反事实示例 - 已经显示用于评估机器学习模型的偏差,例如,对特定的人口组。然而,由于图像的各种特征上的底层的因果结构,生成用于图像的反事实示例是非琐碎的。为了有意义,生成的扰动需要满足因果模型所暗示的约束。我们通过在前瞻性学习推断(ALI)的改进变型中结合结构因果模型(SCM)来提出一种方法,该方法是根据图像的属性之间的因果关系生成反事实。基于所生成的反事实,我们展示了如何解释预先训练的机器学习分类器,评估其偏置,并使用反事实程序缓解偏差。在Morpho-Mnist DataSet上,我们的方法会在质量上产生与基于SCM的Factficuls(DeepScm)的质量相当的反功能,而在更复杂的Celeba DataSet上,我们的方法优于DeepScm在产生高质量的有效反应性时。此外,生成的反事件难以从人类评估实验中的重建图像中无法区分,并且随后使用它们来评估在Celeba数据上培训的标准分类器的公平性。我们表明分类器是偏见的w.r.t.皮肤和头发颜色,以及反事实规则化如何消除这些偏差。
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Causal learning has attracted much attention in recent years because causality reveals the essential relationship between things and indicates how the world progresses. However, there are many problems and bottlenecks in traditional causal learning methods, such as high-dimensional unstructured variables, combinatorial optimization problems, unknown intervention, unobserved confounders, selection bias and estimation bias. Deep causal learning, that is, causal learning based on deep neural networks, brings new insights for addressing these problems. While many deep learning-based causal discovery and causal inference methods have been proposed, there is a lack of reviews exploring the internal mechanism of deep learning to improve causal learning. In this article, we comprehensively review how deep learning can contribute to causal learning by addressing conventional challenges from three aspects: representation, discovery, and inference. We point out that deep causal learning is important for the theoretical extension and application expansion of causal science and is also an indispensable part of general artificial intelligence. We conclude the article with a summary of open issues and potential directions for future work.
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因果关系的概念在人类认知中起着重要作用。在过去的几十年中,在许多领域(例如计算机科学,医学,经济学和教育)中,因果推论已经得到很好的发展。随着深度学习技术的发展,它越来越多地用于针对反事实数据的因果推断。通常,深层因果模型将协变量的特征映射到表示空间,然后设计各种客观优化函数,以根据不同的优化方法公正地估算反事实数据。本文重点介绍了深层因果模型的调查,其核心贡献如下:1)我们在多种疗法和连续剂量治疗下提供相关指标; 2)我们从时间开发和方法分类的角度综合了深层因果模型的全面概述; 3)我们协助有关相关数据集和源代码的详细且全面的分类和分析。
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与CNN的分类,分割或对象检测相比,生成网络的目标和方法根本不同。最初,它们不是作为图像分析工具,而是生成自然看起来的图像。已经提出了对抗性训练范式来稳定生成方法,并已被证明是非常成功的 - 尽管绝不是第一次尝试。本章对生成对抗网络(GAN)的动机进行了基本介绍,并通​​过抽象基本任务和工作机制并得出了早期实用方法的困难来追溯其成功的道路。将显示进行更稳定的训练方法,也将显示出不良收敛及其原因的典型迹象。尽管本章侧重于用于图像生成和图像分析的gan,但对抗性训练范式本身并非特定于图像,并且在图像分析中也概括了任务。在将GAN与最近进入场景的进一步生成建模方法进行对比之前,将闻名图像语义分割和异常检测的架构示例。这将允许对限制的上下文化观点,但也可以对gans有好处。
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A vast amount of expert and domain knowledge is captured by causal structural priors, yet there has been little research on testing such priors for generalization and data synthesis purposes. We propose a novel model architecture, Causal Structural Hypothesis Testing, that can use nonparametric, structural causal knowledge and approximate a causal model's functional relationships using deep neural networks. We use these architectures for comparing structural priors, akin to hypothesis testing, using a deliberate (non-random) split of training and testing data. Extensive simulations demonstrate the effectiveness of out-of-distribution generalization error as a proxy for causal structural prior hypothesis testing and offers a statistical baseline for interpreting results. We show that the variational version of the architecture, Causal Structural Variational Hypothesis Testing can improve performance in low SNR regimes. Due to the simplicity and low parameter count of the models, practitioners can test and compare structural prior hypotheses on small dataset and use the priors with the best generalization capacity to synthesize much larger, causally-informed datasets. Finally, we validate our methods on a synthetic pendulum dataset, and show a use-case on a real-world trauma surgery ground-level falls dataset.
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变异自动编码器(VAE)和其他生成方法不仅对它们的生成特性,而且还具有驱散低维的潜在可变空间的能力。但是,现有的生成模型很少考虑因果关系。我们提出了一个新的基于解码器的框架,称为因果反事实生成模型(CCGM),其中包括一个可训练的因果关系层,其中可以学习因果模型的一部分,而不会显着影响重建忠诚度。通过学习图像语义标签或表格变量之间的因果关系,我们可以分析偏见,干预生成模型并模拟新场景。此外,通过修改因果结构,我们可以在原始训练数据的域之外生成样品,并使用此类反事实模型来驱动数据集。因此,仍然可以使用已知偏差的数据集来训练因果生成模型并学习因果关系,但是我们可以在生成方面产生偏见的数据集。我们提出的方法将因果潜在空间模型与特定的修改相结合,以强调因果关系,从而使对因果层的控制权更加精心控制和学习鲁棒干预框架的能力。我们探索如何更好地分解因果学习和编码/解码会产生更高的因果干预质量。我们还将我们的模型与类似的研究进行了比较,以证明除干预措施以外的明确生成偏差的必要性。我们的初始实验表明,我们的模型可以生成图像和表格数据,并具有高保真度到因果框架上,并适应明确的偏见,以忽略与基线相比,在因果数据中忽略了不希望的关系。
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甚至在没有受限,监督的情况下,也提出了甚至在没有受限或有限的情况下学习普遍陈述的方法。使用适度数量的数据可以微调新的目标任务,或者直接在相应任务中实现显着性能的无奈域中使用的良好普遍表示。这种缓解数据和注释要求为计算机愿景和医疗保健的应用提供了诱人的前景。在本辅导纸上,我们激励了对解散的陈述,目前关键理论和详细的实际构建块和学习此类表示的标准的需求。我们讨论医学成像和计算机视觉中的应用,强调了在示例钥匙作品中进行的选择。我们通过呈现剩下的挑战和机会来结束。
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心血管疾病是全球死亡的主要原因,是一种与年龄有关的疾病。了解衰老期间心脏的形态和功能变化是一个关键的科学问题,其答案将有助于我们定义心血管疾病的重要危险因素并监测疾病进展。在这项工作中,我们提出了一种新型的条件生成模型,以描述衰老过程中心脏3D解剖学的变化。提出的模型是灵活的,可以将多个临床因素(例如年龄,性别)整合到生成过程中。我们在心脏解剖学的大规模横截面数据集上训练该模型,并在横截面和纵向数据集上进行评估。该模型在预测衰老心脏的纵向演化和对其数据分布进行建模方面表现出了出色的表现。
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Counterfactual explanation is a common class of methods to make local explanations of machine learning decisions. For a given instance, these methods aim to find the smallest modification of feature values that changes the predicted decision made by a machine learning model. One of the challenges of counterfactual explanation is the efficient generation of realistic counterfactuals. To address this challenge, we propose VCNet-Variational Counter Net-a model architecture that combines a predictor and a counterfactual generator that are jointly trained, for regression or classification tasks. VCNet is able to both generate predictions, and to generate counterfactual explanations without having to solve another minimisation problem. Our contribution is the generation of counterfactuals that are close to the distribution of the predicted class. This is done by learning a variational autoencoder conditionally to the output of the predictor in a join-training fashion. We present an empirical evaluation on tabular datasets and across several interpretability metrics. The results are competitive with the state-of-the-art method.
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This invited review discusses causal learning in the context of robotic intelligence. The paper introduced the psychological findings on causal learning in human cognition, then it introduced the traditional statistical solutions on causal discovery and causal inference. The paper reviewed recent deep causal learning algorithms with a focus on their architectures and the benefits of using deep nets and discussed the gap between deep causal learning and the needs of robotic intelligence.
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代表学习者认为,解开变异的因素已经证明是在解决各种现实世界的关切方面是重要的,如公平和可意识。最初由具有独立假设的无监督模型组成,最近,监督和相关特征较弱,但没有生成过程的因果关系。相比之下,我们在原因生成过程的制度下工作,因为生成因子是独立的,或者可能被一组观察或未观察到的混乱困惑。我们通过解散因果过程的概念对解开表示的分析。我们激励对新指标和数据集进行研究,以研究因果解剖和提出两个评估指标和数据集。我们展示了我们的指标捕获了解开了因果过程的探索。最后,我们利用我们的指标和数据集对艺术艺术状态的实证研究进行了脱扣代表学习者,以从因果角度来评估它们。
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随着脑成像技术和机器学习工具的出现,很多努力都致力于构建计算模型来捕获人脑中的视觉信息的编码。最具挑战性的大脑解码任务之一是通过功能磁共振成像(FMRI)测量的脑活动的感知自然图像的精确重建。在这项工作中,我们调查了来自FMRI的自然图像重建的最新学习方法。我们在架构设计,基准数据集和评估指标方面检查这些方法,并在标准化评估指标上呈现公平的性能评估。最后,我们讨论了现有研究的优势和局限,并提出了潜在的未来方向。
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Although understanding and characterizing causal effects have become essential in observational studies, it is challenging when the confounders are high-dimensional. In this article, we develop a general framework $\textit{CausalEGM}$ for estimating causal effects by encoding generative modeling, which can be applied in both binary and continuous treatment settings. Under the potential outcome framework with unconfoundedness, we establish a bidirectional transformation between the high-dimensional confounders space and a low-dimensional latent space where the density is known (e.g., multivariate normal distribution). Through this, CausalEGM simultaneously decouples the dependencies of confounders on both treatment and outcome and maps the confounders to the low-dimensional latent space. By conditioning on the low-dimensional latent features, CausalEGM can estimate the causal effect for each individual or the average causal effect within a population. Our theoretical analysis shows that the excess risk for CausalEGM can be bounded through empirical process theory. Under an assumption on encoder-decoder networks, the consistency of the estimate can be guaranteed. In a series of experiments, CausalEGM demonstrates superior performance over existing methods for both binary and continuous treatments. Specifically, we find CausalEGM to be substantially more powerful than competing methods in the presence of large sample sizes and high dimensional confounders. The software of CausalEGM is freely available at https://github.com/SUwonglab/CausalEGM.
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生成的对抗网络(GAN)是在众多领域成功使用的一种强大的深度学习模型。它们属于一个称为生成方法的更广泛的家族,该家族通过从真实示例中学习样本分布来生成新数据。在临床背景下,与传统的生成方法相比,GAN在捕获空间复杂,非线性和潜在微妙的疾病作用方面表现出增强的能力。这篇综述评估了有关gan在各种神经系统疾病的成像研究中的应用的现有文献,包括阿尔茨海默氏病,脑肿瘤,脑老化和多发性硬化症。我们为每个应用程序提供了各种GAN方法的直观解释,并进一步讨论了在神经影像学中利用gans的主要挑战,开放问题以及有希望的未来方向。我们旨在通过强调如何利用gan来支持临床决策,并有助于更好地理解脑部疾病的结构和功能模式,从而弥合先进的深度学习方法和神经病学研究之间的差距。
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Modeling lies at the core of both the financial and the insurance industry for a wide variety of tasks. The rise and development of machine learning and deep learning models have created many opportunities to improve our modeling toolbox. Breakthroughs in these fields often come with the requirement of large amounts of data. Such large datasets are often not publicly available in finance and insurance, mainly due to privacy and ethics concerns. This lack of data is currently one of the main hurdles in developing better models. One possible option to alleviating this issue is generative modeling. Generative models are capable of simulating fake but realistic-looking data, also referred to as synthetic data, that can be shared more freely. Generative Adversarial Networks (GANs) is such a model that increases our capacity to fit very high-dimensional distributions of data. While research on GANs is an active topic in fields like computer vision, they have found limited adoption within the human sciences, like economics and insurance. Reason for this is that in these fields, most questions are inherently about identification of causal effects, while to this day neural networks, which are at the center of the GAN framework, focus mostly on high-dimensional correlations. In this paper we study the causal preservation capabilities of GANs and whether the produced synthetic data can reliably be used to answer causal questions. This is done by performing causal analyses on the synthetic data, produced by a GAN, with increasingly more lenient assumptions. We consider the cross-sectional case, the time series case and the case with a complete structural model. It is shown that in the simple cross-sectional scenario where correlation equals causation the GAN preserves causality, but that challenges arise for more advanced analyses.
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决策者需要在采用新的治疗政策之前预测结果的发展,该政策定义了何时以及如何连续地影响结果的治疗序列。通常,预测介入的未来结果轨迹的算法将未来治疗的固定顺序作为输入。这要么忽略了未来治疗对结果之前的结果的依赖性,要么隐含地假设已知治疗政策,因此排除了该政策未知或需要反事实分析的情况。为了应对这些局限性,我们开发了一种用于治疗和结果的联合模型,该模型允许估计处理策略和顺序治疗(OUT COMECTION数据)的影响。它可以回答有关治疗政策干预措施的介入和反事实查询,因为我们使用有关血糖进展的现实数据显示,并在此基础上进行了模拟研究。
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在存在潜在变量的情况下,从观察数据中估算因果关系的效果有时会导致虚假关系,这可能被错误地认为是因果关系。这是许多领域的重要问题,例如金融和气候科学。我们提出了序性因果效应变异自动编码器(SCEVAE),这是一种在隐藏混杂下的时间序列因果关系分析的新方法。它基于CEVAE框架和复发性神经网络。通过基于Pearl的Do-Calculus使用直接因果标准来计算因果链接的混杂变量强度。我们通过将其应用于具有线性和非线性因果链接的合成数据集,以显示SCEVAE的功效。此外,我们将方法应用于真实的气溶胶气候观察数据。我们将我们的方法与在合成数据上有或没有替代混杂因素的时间序列变形方法进行比较。我们证明我们的方法通过将两种方法与地面真理进行比较来表现更好。对于真实数据,我们使用因果链接的专家知识,并显示正确的代理变量的使用如何帮助数据重建。
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