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。

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。

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。

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。

Observational studies are rising in importance due to the widespread accumulation of data in fields such as healthcare, education, employment and ecology. We consider the task of answering counterfactual questions such as, "Would this patient have lower blood sugar had she received a different medication?". We propose a new algorithmic framework for counterfactual inference which brings together ideas from domain adaptation and representation learning. In addition to a theoretical justification, we perform an empirical comparison with previous approaches to causal inference from observational data. Our deep learning algorithm significantly outperforms the previous state-of-the-art.

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。

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.

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。

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.

在推荐系统中，一个普遍的挑战是冷门问题，在系统中，相互作用非常有限。为了应对这一挑战，最近，许多作品将元优化的想法介绍到建议方案中，即学习仅通过过去的几个交互项目来学习用户偏好。核心想法是为所有用户学习全局共享的元启动参数，并分别为每个用户迅速调整其本地参数。他们的目的是在各种用户的偏好学习中得出一般知识，以便通过博学的先验和少量培训数据迅速适应未来的新用户。但是，以前的作品表明，推荐系统通常容易受到偏见和不公平的影响。尽管元学习成功地通过冷启动提高了推荐性能，但公平性问题在很大程度上被忽略了。在本文中，我们提出了一个名为Clover的全面的公平元学习框架，以确保元学习的推荐模型的公平性。我们系统地研究了三种公平性 - 个人公平，反事实公平和推荐系统中的群体公平，并建议通过多任务对抗学习方案满足所有三种类型。我们的框架提供了一种通用的培训范式，适用于不同的元学习推荐系统。我们证明了三叶草对三个现实世界数据集的代表性元学习用户偏好估计器的有效性。经验结果表明，三叶草可以实现全面的公平性，而不会恶化整体的冷淡建议性能。

Observational studies have recently received significant attention from the machine learning community due to the increasingly available non-experimental observational data and the limitations of the experimental studies, such as considerable cost, impracticality, small and less representative sample sizes, etc. In observational studies, de-confounding is a fundamental problem of individualised treatment effects (ITE) estimation. This paper proposes disentangled representations with adversarial training to selectively balance the confounders in the binary treatment setting for the ITE estimation. The adversarial training of treatment policy selectively encourages treatment-agnostic balanced representations for the confounders and helps to estimate the ITE in the observational studies via counterfactual inference. Empirical results on synthetic and real-world datasets, with varying degrees of confounding, prove that our proposed approach improves the state-of-the-art methods in achieving lower error in the ITE estimation.

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.

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.

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.

训练因果效果变分性自身摩托（CEVAE）以预测给定的观察治疗数据的结果，而使用重要性采样均匀的处理分布训练均匀治疗变分性自身培训（UTVAE）。在本文中，我们表明，通过减轻训练训练以测试时间发生的分布换档，使用对观察治疗分布的均匀处理导致更好的因果化推断。我们还探讨了统一和观察治疗分布的组合，推断和生成网络培训目标，以找到更好的培训程序，用于推断治疗效果。实验，我们发现所提出的Utvae在综合效应误差估计比Sycleiny和IHDP数据集上的CEVAE估计的估计是更好的绝对平均处理效果误差和精度。

为目标疾病开发新药物是一项耗时且昂贵的任务，药物重新利用已成为药物开发领域的流行话题。随着许多健康索赔数据可用，已经对数据进行了许多研究。现实世界的数据嘈杂，稀疏，并且具有许多混杂因素。此外，许多研究表明，药物的作用在人群中是异质的。近年来已经出现了许多有关估计异构治疗效果（HTE）（HTE）的高级机器学习模型，并已应用于计量经济学和机器学习社区。这些研究将医学和药物开发视为主要应用领域，但是从HTE方法论到药物开发的转化研究有限。我们旨在将HTE方法介绍到医疗保健领域，并在通过基准实验进行医疗保健行政索赔数据进行基准实验时提供可行性考虑。另外，我们希望使用基准实验来展示如何将模型应用于医疗保健研究时如何解释和评估模型。通过将最近的HTE技术引入生物医学信息学社区的广泛读者，我们希望通过机器学习促进广泛采用因果推断。我们还希望提供HTE具有个性化药物有效性的可行性。

机器学习系统通常假设训练和测试分布是相同的。为此，关键要求是开发可以概括到未经看不见的分布的模型。领域泛化（DG），即分销概括，近年来引起了越来越令人利益。域概括处理了一个具有挑战性的设置，其中给出了一个或几个不同但相关域，并且目标是学习可以概括到看不见的测试域的模型。多年来，域概括地区已经取得了巨大进展。本文提出了对该地区最近进步的首次审查。首先，我们提供了域泛化的正式定义，并讨论了几个相关领域。然后，我们彻底审查了与域泛化相关的理论，并仔细分析了泛化背后的理论。我们将最近的算法分为三个类：数据操作，表示学习和学习策略，并为每个类别详细介绍几种流行的算法。第三，我们介绍常用的数据集，应用程序和我们的开放源代码库进行公平评估。最后，我们总结了现有文学，并为未来提供了一些潜在的研究主题。

Transfer learning aims at improving the performance of target learners on target domains by transferring the knowledge contained in different but related source domains. In this way, the dependence on a large number of target domain data can be reduced for constructing target learners. Due to the wide application prospects, transfer learning has become a popular and promising area in machine learning. Although there are already some valuable and impressive surveys on transfer learning, these surveys introduce approaches in a relatively isolated way and lack the recent advances in transfer learning. Due to the rapid expansion of the transfer learning area, it is both necessary and challenging to comprehensively review the relevant studies. This survey attempts to connect and systematize the existing transfer learning researches, as well as to summarize and interpret the mechanisms and the strategies of transfer learning in a comprehensive way, which may help readers have a better understanding of the current research status and ideas. Unlike previous surveys, this survey paper reviews more than forty representative transfer learning approaches, especially homogeneous transfer learning approaches, from the perspectives of data and model. The applications of transfer learning are also briefly introduced. In order to show the performance of different transfer learning models, over twenty representative transfer learning models are used for experiments. The models are performed on three different datasets, i.e., Amazon Reviews, Reuters-21578, and Office-31. And the experimental results demonstrate the importance of selecting appropriate transfer learning models for different applications in practice.

大型观察数据越来越多地提供健康，经济和社会科学等学科，研究人员对因果问题而不是预测感兴趣。在本文中，从旨在调查参与学校膳食计划对健康指标的实证研究，研究了使用非参数回归的方法估算异质治疗效果的问题。首先，我们介绍了与观察或非完全随机数据进行因果推断相关的设置和相关的问题，以及如何在统计学习工具的帮助下解决这些问题。然后，我们审查并制定现有最先进的框架的统一分类，允许通过非参数回归模型来估算单个治疗效果。在介绍模型选择问题的简要概述后，我们说明了一些关于三种不同模拟研究的方法的性能。我们通过展示一些关于学校膳食计划数据的实证分析的一些方法的使用来结束。

Generalization capability to unseen domains is crucial for machine learning models when deploying to real-world conditions. We investigate the challenging problem of domain generalization, i.e., training a model on multi-domain source data such that it can directly generalize to target domains with unknown statistics. We adopt a model-agnostic learning paradigm with gradient-based meta-train and meta-test procedures to expose the optimization to domain shift. Further, we introduce two complementary losses which explicitly regularize the semantic structure of the feature space. Globally, we align a derived soft confusion matrix to preserve general knowledge about inter-class relationships. Locally, we promote domainindependent class-specific cohesion and separation of sample features with a metric-learning component. The effectiveness of our method is demonstrated with new state-of-the-art results on two common object recognition benchmarks. Our method also shows consistent improvement on a medical image segmentation task.