数据驱动的社会事件预测方法利用相关的历史信息来预测未来的事件。这些方法依赖于历史标记数据,并且当数据有限或质量差时无法准确地预测事件。研究事件之间的因果效应超出相关性分析,并且可以有助于更强大的事件预测。然而,由于若干因素,在数据驱动事件预测中纳入因果区分析是具有挑战性的:(i)事件发生在复杂和充满活力的社交环境中。许多未观察到的变量,即隐藏的混乱,影响潜在的原因和结果。 (ii)给予时尚非独立和相同分布的(非IID)数据,为准确的因果效应估计建模隐藏的混淆并不差。在这项工作中,我们介绍了一个深入的学习框架,将因果效应估计整合到事件预测中。我们首先研究了从时空属性的观察事件数据的单个治疗效果(ITE)估计的问题,并提出了一种新的因果推断模型来估计ites。然后,我们将学习的事件相关的因果信息纳入事件预测作为先验知识。引入了两个强大的学习模块,包括特征重载模块和近似约束损耗,以实现先验知识注入。我们通过将学习的因果信息送入不同的深度学习方法,评估了真实世界事件数据集的提出的因果推断模型,并验证了在事件预测中提出的强大学习模块的有效性。实验结果展示了社会事件中拟议的因果推断模型的强度,并展示了社会事件预测中强大的学习模块的有益特性。
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因果推断能够估计治疗效果(即,治疗结果的因果效果),使各个领域的决策受益。本研究中的一个基本挑战是观察数据的治疗偏见。为了提高对因果推断的观察研究的有效性,基于代表的方法作为最先进的方法表明了治疗效果估计的卓越性能。基于大多数基于表示的方法假设所有观察到的协变量都是预处理的(即,不受治疗影响的影响),并学习这些观察到的协变量的平衡表示,以估算治疗效果。不幸的是,这种假设往往在实践中往往是太严格的要求,因为一些协调因子是通过对治疗的干预进行改变(即,后治疗)来改变。相比之下,从不变的协变量中学到的平衡表示因此偏置治疗效果估计。
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人口级社会事件,如民事骚乱和犯罪,往往对我们的日常生活产生重大影响。预测此类事件对于决策和资源分配非常重要。由于缺乏关于事件发生的真实原因和潜在机制的知识,事件预测传统上具有挑战性。近年来,由于两个主要原因,研究事件预测研究取得了重大进展:(1)机器学习和深度学习算法的开发和(2)社交媒体,新闻来源,博客,经济等公共数据的可访问性指标和其他元数据源。软件/硬件技术中的数据的爆炸性增长导致了社会事件研究中的深度学习技巧的应用。本文致力于提供社会事件预测的深层学习技术的系统和全面概述。我们专注于两个社会事件的域名:\ Texit {Civil unrest}和\ texit {犯罪}。我们首先介绍事件预测问题如何作为机器学习预测任务制定。然后,我们总结了这些问题的数据资源,传统方法和最近的深度学习模型的发展。最后,我们讨论了社会事件预测中的挑战,并提出了一些有希望的未来研究方向。
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HyperGraphs为在节点之间建模多路相互作用提供了有效的抽象,每个HyperEdge都可以连接任何数量的节点。与大多数利用统计依赖性的研究不同,我们从因果关系的角度研究了超图。具体而言,在本文中,我们重点介绍了对超图的个人治疗效果(ITE)估计的问题,旨在估算干预措施(例如,佩戴脸部覆盖)将对结果(例如,Covid-19感染)的因果影响(例如,Covid-19感染)影响。每个节点。关于ITE估计的现有作品假设一个人的结果不应受到其他个体的治疗作业的影响(即无干扰),或者假设仅在普通图中的成对相关个体之间存在干扰。我们认为,这些假设对现实世界中的超图可能是不现实的,在现实世界中,高阶干扰可能会影响由于存在组相互作用而导致的最终ITE估计。在这项工作中,我们研究了高阶干扰建模,并提出了一个由HyperGraph神经网络提供支持的新因果学习框架。对现实世界超图的广泛实验验证了我们框架优于现有基线的优势。
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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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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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Causal inference is the process of using assumptions, study designs, and estimation strategies to draw conclusions about the causal relationships between variables based on data. This allows researchers to better understand the underlying mechanisms at work in complex systems and make more informed decisions. In many settings, we may not fully observe all the confounders that affect both the treatment and outcome variables, complicating the estimation of causal effects. To address this problem, a growing literature in both causal inference and machine learning proposes to use Instrumental Variables (IV). This paper serves as the first effort to systematically and comprehensively introduce and discuss the IV methods and their applications in both causal inference and machine learning. First, we provide the formal definition of IVs and discuss the identification problem of IV regression methods under different assumptions. Second, we categorize the existing work on IV methods into three streams according to the focus on the proposed methods, including two-stage least squares with IVs, control function with IVs, and evaluation of IVs. For each stream, we present both the classical causal inference methods, and recent developments in the machine learning literature. Then, we introduce a variety of applications of IV methods in real-world scenarios and provide a summary of the available datasets and algorithms. Finally, we summarize the literature, discuss the open problems and suggest promising future research directions for IV methods and their applications. We also develop a toolkit of IVs methods reviewed in this survey at https://github.com/causal-machine-learning-lab/mliv.
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我们定期考虑在实践中回答反事实问题,例如“糖尿病患者会选择另一种药物,会更好吗?”。观察性研究在回答此类问题的显着性上增长,因为它们的广泛积累和比随机对照试验(RCT)比较容易获得的。最近,一些作品将表示和域的适应性引入了反事实推断。但是,大多数目前的作品都集中在二进制治疗的设置上。他们都没有认为不同治疗的样本量不平衡,尤其是由于固有的用户偏好,某些治疗组中的数据示例相对有限。在本文中,我们为反事实推断设计了一种新的算法框架,从元学习来估算单个治疗效果(元地铁)以填补上述研究空白,尤其是考虑多种不平衡治疗方法。具体而言,我们将反事实推断的治疗组之间的数据发作视为元学习任务。我们从一组有足够样品的源治疗组中训练一个元学习者,并通过梯度下降进行梯度下降,而在目标治疗中样本有限。此外,我们引入了两个互补的损失。一个是多种来源治疗的监督损失。提出了与各个治疗组之间潜在分布对齐的另一个损失,以减少差异。我们在两个现实世界数据集上执行实验,以评估推理准确性和概括能力。实验结果表明,模型元地铁匹配/跑赢大的方法。
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There is intense interest in applying machine learning to problems of causal inference in fields such as healthcare, economics and education. In particular, individual-level causal inference has important applications such as precision medicine. We give a new theoretical analysis and family of algorithms for predicting individual treatment effect (ITE) from observational data, under the assumption known as strong ignorability. The algorithms learn a "balanced" representation such that the induced treated and control distributions look similar. We give a novel, simple and intuitive generalization-error bound showing that the expected ITE estimation error of a representation is bounded by a sum of the standard generalization-error of that representation and the distance between the treated and control distributions induced by the representation. We use Integral Probability Metrics to measure distances between distributions, deriving explicit bounds for the Wasserstein and Maximum Mean Discrepancy (MMD) distances. Experiments on real and simulated data show the new algorithms match or outperform the state-of-the-art.
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接触犯罪和暴力会损害个人的生活质量和社区的经济增长。鉴于机器学习的迅速发展,需要探索自动解决方案以防止犯罪。随着细粒度的城市和公共服务数据的可用性越来越多,最近融合了这种跨域信息以促进犯罪预测的激增。通过捕获有关社会结构,环境和犯罪趋势的信息,现有的机器学习预测模型从不同观点探索了动态犯罪模式。但是,这些方法主要将这种多源知识转换为隐性和潜在表示(例如,学区的嵌入),这仍然是研究显式因素对幕后犯罪发生的影响的影响仍然是一个挑战。在本文中,我们提出了一个时空的元数据指导性犯罪预测(STMEC)框架,以捕获犯罪行为的动态模式,并明确地表征了环境和社会因素如何相互互动以产生预测。广泛的实验表明,与其他先进的时空模型相比,STMEC的优越性,尤其是在预测重罪(例如使用危险武器的抢劫和袭击)时。
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Determining causal effects of temporal multi-intervention assists decision-making. Restricted by time-varying bias, selection bias, and interactions of multiple interventions, the disentanglement and estimation of multiple treatment effects from individual temporal data is still rare. To tackle these challenges, we propose a comprehensive framework of temporal counterfactual forecasting from an individual multiple treatment perspective (TCFimt). TCFimt constructs adversarial tasks in a seq2seq framework to alleviate selection and time-varying bias and designs a contrastive learning-based block to decouple a mixed treatment effect into separated main treatment effects and causal interactions which further improves estimation accuracy. Through implementing experiments on two real-world datasets from distinct fields, the proposed method shows satisfactory performance in predicting future outcomes with specific treatments and in choosing optimal treatment type and timing than state-of-the-art methods.
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选择每个患者的最佳治疗计划需要随着时间的推移而准确地预测其结果轨迹的函数。虽然大型观察数据集构成丰富的信息来源,但他们也包含偏差,因为处理很少在实践中随机分配。为了提供准确和无偏见的预测,我们介绍了解除戒备的反事实经常性网络(DCRN),一种新的序列到序列架构,其通过学习患者历史的时间随时间估计治疗结果,这些历史记录被解除为三个单独的潜在因子:治疗因素,影响只有治疗选择;结果因素,影响结果;和一个混杂因素,影响两者。通过架构,完全受到治疗影响的因果结构随着时间的推移,我们推进预测准确性和疾病的理解,因为我们的建筑允许从业者推断哪个患者的轨迹影响哪些患者的轨迹,对比该领域的其他方法对比其他方法。我们证明DCRN在预测治疗响应中的当前最先进的方法,在实际和模拟数据中优于最新的方法。
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制定和实施基于AI的解决方案有助于国家和联邦政府机构,研究机构和商业公司加强决策过程,自动化连锁业务,减少自然和人力资源的消费。与此同时,实践中使用的大多数AI方法只能表示为“黑匣子”并遭受缺乏透明度。这最终可能导致意外的结果和破坏在这种系统中的信任。因此,至关重要,不仅要开发有效和强大的AI系统,而且为了确保其内部过程可解释和公平。我们本章的目标是利用美国经济技术部门的示例,介绍具有高影响决策的AI系统的保证方法的主题。我们通过提供技术经济数据集的因果试验,我们解释了这些领域如何从数据集的关键指标之间揭示致命关系。审查了几种因果推断方法和AI保证技术,并对数据转换为图形结构数据集。
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Learning individual-level causal effects from observational data, such as inferring the most effective medication for a specific patient, is a problem of growing importance for policy makers. The most important aspect of inferring causal effects from observational data is the handling of confounders, factors that affect both an intervention and its outcome. A carefully designed observational study attempts to measure all important confounders. However, even if one does not have direct access to all confounders, there may exist noisy and uncertain measurement of proxies for confounders. We build on recent advances in latent variable modeling to simultaneously estimate the unknown latent space summarizing the confounders and the causal effect. Our method is based on Variational Autoencoders (VAE) which follow the causal structure of inference with proxies. We show our method is significantly more robust than existing methods, and matches the state-of-the-art on previous benchmarks focused on individual treatment effects.
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由于选择偏差,观察数据估算平均治疗效果(ATE)是有挑战性的。现有作品主要以两种方式应对这一挑战。一些研究人员建议构建满足正交条件的分数函数,该函数确保已建立的估计量“正交”更加健壮。其他人探索表示模型,以实现治疗组和受控群体之间的平衡表示。但是,现有研究未能进行1)在表示空间中歧视受控单元以避免过度平衡的问题; 2)充分利用“正交信息”。在本文中,我们提出了一个基于最新协变量平衡表示方法和正交机器学习理论的中等平衡的表示学习(MBRL)框架。该框架可保护表示形式免于通过多任务学习过度平衡。同时,MBRL将噪声正交性信息纳入培训和验证阶段,以实现更好的ATE估计。与现有的最新方法相比,基准和模拟数据集的全面实验表明,我们方法对治疗效应估计的优越性和鲁棒性。
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COVID-19的大流行提出了对多个领域决策者的流行预测的重要性,从公共卫生到整个经济。虽然预测流行进展经常被概念化为类似于天气预测,但是它具有一些关键的差异,并且仍然是一项非平凡的任务。疾病的传播受到人类行为,病原体动态,天气和环境条件的多种混杂因素的影响。由于政府公共卫生和资助机构的倡议,捕获以前无法观察到的方面的丰富数据来源的可用性增加了研究的兴趣。这尤其是在“以数据为中心”的解决方案上进行的一系列工作,这些解决方案通过利用非传统数据源以及AI和机器学习的最新创新来增强我们的预测能力的潜力。这项调查研究了各种数据驱动的方法论和实践进步,并介绍了一个概念框架来导航它们。首先,我们列举了与流行病预测相关的大量流行病学数据集和新的数据流,捕获了各种因素,例如有症状的在线调查,零售和商业,流动性,基因组学数据等。接下来,我们将讨论关注最近基于数据驱动的统计和深度学习方法的方法和建模范式,以及将机械模型知识域知识与统计方法的有效性和灵活性相结合的新型混合模型类别。我们还讨论了这些预测系统的现实部署中出现的经验和挑战,包括预测信息。最后,我们重点介绍了整个预测管道中发现的一些挑战和开放问题。
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因果推论在电子商务和精确医学等各个领域都有广泛的应用,其性能在很大程度上取决于对个体治疗效果(ITE)的准确估计。通常,通过在其各个样品空间中分别对处理和控制响应函数进行建模来预测ITE。但是,这种方法通常会在实践中遇到两个问题,即治疗偏见引起的治疗组和对照组之间的分布分布以及其人口规模的显着样本失衡。本文提出了深层的整个空间跨网络(DESCN),以从端到端的角度进行建模治疗效果。 DESCN通过多任务学习方式捕获了治疗倾向,反应和隐藏治疗效果的综合信息。我们的方法共同学习了整个样品空间中的治疗和反应功能,以避免治疗偏见,并采用中间伪治疗效应预测网络来减轻样品失衡。从电子商务凭证分销业务的合成数据集和大规模生产数据集进行了广泛的实验。结果表明,DESCN可以成功提高ITE估计的准确性并提高提升排名的性能。发布生产数据集和源代码的样本是为了促进社区的未来研究,据我们所知,这是首个大型公共偏见的因果推理数据集。
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由于混杂偏见的复杂情况,使用观察数据估算治疗效果,尤其是个性化治疗效果(ITE),这是具有挑战性的。纵向观察数据估算治疗效果的现有方法通常是基于“不满意”的强烈假设,在现实世界实践中很难实现。在本文中,我们提出了变异的时间变形器(VTD),这种方法使用代理(即用于无法观察到的变量)来利用纵向设置中深层嵌入的方法。具体而言,VTD利用观察到的代理学习隐藏的嵌入,以反映观测数据中真正隐藏的混杂因素。因此,我们的VTD方法不依赖“不符”假设。我们在合成和实际临床数据上测试了VTD方法,结果表明,与其他现有模型相比,隐藏混杂性是主要偏见时我们的方法有效。
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Time series anomaly detection has applications in a wide range of research fields and applications, including manufacturing and healthcare. The presence of anomalies can indicate novel or unexpected events, such as production faults, system defects, or heart fluttering, and is therefore of particular interest. The large size and complex patterns of time series have led researchers to develop specialised deep learning models for detecting anomalous patterns. This survey focuses on providing structured and comprehensive state-of-the-art time series anomaly detection models through the use of deep learning. It providing a taxonomy based on the factors that divide anomaly detection models into different categories. Aside from describing the basic anomaly detection technique for each category, the advantages and limitations are also discussed. Furthermore, this study includes examples of deep anomaly detection in time series across various application domains in recent years. It finally summarises open issues in research and challenges faced while adopting deep anomaly detection models.
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