Selecting causal inference models for estimating individualized treatment effects (ITE) from observational data presents a unique challenge since the counterfactual outcomes are never observed. The problem is challenged further in the unsupervised domain adaptation (UDA) setting where we only have access to labeled samples in the source domain, but desire selecting a model that achieves good performance on a target domain for which only unlabeled samples are available. Existing techniques for UDA model selection are designed for the predictive setting. These methods examine discriminative density ratios between the input covariates in the source and target domain and do not factor in the model's predictions in the target domain. Because of this, two models with identical performance on the source domain would receive the same risk score by existing methods, but in reality, have significantly different performance in the test domain. We leverage the invariance of causal structures across domains to propose a novel model selection metric specifically designed for ITE methods under the UDA setting. In particular, we propose selecting models whose predictions of interventions' effects satisfy known causal structures in the target domain. Experimentally, our method selects ITE models that are more robust to covariate shifts on several healthcare datasets, including estimating the effect of ventilation in COVID-19 patients from different geographic locations.
School of Computing, National University of Singapore（新加坡国立大学计算机学院）；Institute of Data Science, National University of Singapore（新加坡国立大学数据科学研究所）；Singapore-MIT Alliance for Research and Technology（新加坡-麻省理工联合研究技术联盟）
Bayesian Meta-Learning with Expert Feedback for Task-Shift Adaptation through Causal Embeddings
Department of Computer Science, Aalto University, Espoo, Finland（Aalto大学计算机科学系）；ELLIS Institute Finland, Espoo, Finland（芬兰ELLIS研究所）；Department of Computer Science, University of Manchester, United Kingdom（曼彻斯特大学计算机科学系）
IBM Research（IBM研究院）；University of Amsterdam（阿姆斯特丹大学）；Radboud University Nijmegen（内梅亨大学）
An important goal common to domain adaptation and causal inference is to make accurate predictions when the distributions for the source (or training) domain(s) and target (or test) domain(s) differ. In many cases, these different distributions can be modeled as different contexts of a single underlying system, in which each distribution corresponds to a different perturbation of the system, or in causal terms, an intervention. We focus on a class of such causal domain adaptation problems, where data for one or more source domains are given, and the task is to predict the distribution of a cer…
Purdue University（普渡大学）；Kalinga Institute of Industrial Technology（卡林加工业技术学院）；University of South Carolina（南卡罗来纳大学）
One of the most critical problems in transfer learning is the task of domain adaptation, where the goal is to apply an algorithm trained in one or more source domains to a different (but related) target domain. This paper deals with domain adaptation in the presence of covariate shift while invariances exist across domains. One of the main limitations of existing causal inference methods for solving this problem is scalability. To overcome this difficulty, we propose SCTL, an algorithm that avoids an exhaustive search and identifies invariant causal features across source and target domains ba…
Domain Adaptation (DA) is a typical problem in machine learning that aims to transfer the model trained on source domain to target domain with different distribution. Causal DA is a special case of DA that solves the problem from the view of causality. It embeds the probabilistic relationships in multiple domains in a larger causal structure network of a system and tries to find the causal source (or intervention) on the system as the reason of distribution drifts of the system states across domains. In this sense, causal DA is transformed as a causal discovery problem that finds invariant rep…
Hefei University of Technology（合肥工业大学）；School of Computer Science and Information Engineering, Hefei University of Technology（合肥工业大学计算机与信息学院）；Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology), Ministry of Education（大数据知识工程教育部重点实验室）；Shanxi University（山西大学）；School of Computer and Information Technology, Shanxi University（山西大学计算机与信息技术学院）；University of South Australia（南澳大学）；UniSA STEM, University of South Australia（南澳大学UniSA STEM学院）
Domain adaptation solves the learning problem in a target domain by leveraging the knowledge in a relevant source domain. While remarkable advances have been made, almost all existing domain adaptation methods heavily require large amounts of unlabeled target domain data for learning domain invariant representations to achieve good generalizability on the target domain. In fact, in many real-world applications, target domain data may not always be available. In this paper, we study the cases where at the training phase the target domain data is unavailable and only well-labeled source domain d…
We study few-shot supervised domain adaptation (DA) for regression problems, where only a few labeled target domain data and many labeled source domain data are available. Many of the current DA methods base their transfer assumptions on either parametrized distribution shift or apparent distribution similarities, e.g., identical conditionals or small distributional discrepancies. However, these assumptions may preclude the possibility of adaptation from intricately shifted and apparently very different distributions. To overcome this problem, we propose mechanism transfer, a meta-distribution…
How Useful is Causal Invariance for Domain Adaptation in Finite-Sample Settings?
Department of Computer Science, ETH Zurich（苏黎世联邦理工学院计算机科学系）；Causal Artificial Intelligence Lab, Columbia University（哥伦比亚大学因果人工智能实验室）；Department of Statistics, Columbia University（哥伦比亚大学统计系）
University of Pittsburgh（匹兹堡大学）；Carnegie Mellon University（卡内基梅隆大学）；The University of Sydney（悉尼大学）
An essential problem in domain adaptation is to understand and make use of distribution changes across domains. For this purpose, we first propose a flexible Generative Domain Adaptation Network (G-DAN) with specific latent variables to capture changes in the generating process of features across domains. By explicitly modeling the changes, one can even generate data in new domains using the generating process with new values for the latent variables in G-DAN. In practice, the process to generate all features together may involve high-dimensional latent variables, requiring dealing with distri…
Guangdong University of Technology（广东工业大学）；Kaer Pte. Ltd（Kaer私人有限公司）
Data-driven models are becoming essential parts in modern mechanical systems, commonly used to capture the behavior of various equipment and varying environmental characteristics. Despite the advantages of these data-driven models on excellent adaptivity to high dynamics and aging equipment, they are usually hungry to massive labels over historical data, mostly contributed by human engineers at an extremely high cost. The label demand is now the major limiting factor to modeling accuracy, hindering the fulfillment of visions for applications. Fortunately, domain adaptation enhances the model g…
Continual Test-Time Adaptation for Single Image Defocus Deblurring via Causal Siamese Networks
Institute of Software Chinese Academy of Sciences（中国科学院软件研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Renmin University of China（中国人民大学）；the Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；the Hong Kong University of Science and Technology（香港科技大学）
Time Series Domain Adaptation via Latent Invariant Causal Mechanism
School of Computer Science, Guangdong University of Technology（广东工业大学计算机学院）；Peng Cheng Laboratory（鹏城实验室）；School of Computing, Guangdong University of Technology（广东工业大学计算机学院）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Institute for Infocomm Research, A*STAR（新加坡科技研究局信息通信研究所）；Centre for Frontier AI Research, A*STAR（新加坡科技研究局前沿人工智能研究中心）；Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）
Guangdong University of Technology（广东工业大学）；Huawei Noah’s Ark Lab（华为诺亚方舟实验室）；Pazhou Lab（琶洲实验室）
Domain adaptation on time series data is an important but challenging task. Most of the existing works in this area are based on the learning of the domain-invariant representation of the data with the help of restrictions like MMD. However, such extraction of the domain-invariant representation is a non-trivial task for time series data, due to the complex dependence among the timestamps. In detail, in the fully dependent time series, a small change of the time lags or the offsets may lead to difficulty in the domain invariant extraction. Fortunately, the stability of the causality inspired u…
Michigan Technological University（密歇根理工大学）；University of Hong Kong（香港大学）
Given the rapidly changing machine learning environments and expensive data labeling, semi-supervised domain adaptation (SSDA) is imperative when the labeled data from the source domain is statistically different from the partially labeled data from the target domain. Most prior SSDA research is centrally performed, requiring access to both source and target data. However, data in many fields nowadays is generated by distributed end devices. Due to privacy concerns, the data might be locally stored and cannot be shared, resulting in the ineffectiveness of existing SSDA research. This paper pro…
University of Amsterdam（阿姆斯特丹大学）；Vrije Universiteit Amsterdam（阿姆斯特丹自由大学）
Machine learning models trained with purely observational data and the principle of empirical risk minimization \citep{vapnik_principles_1992} can fail to generalize to unseen domains. In this paper, we focus on the case where the problem arises through spurious correlation between the observed domains and the actual task labels. We find that many domain generalization methods do not explicitly take this spurious correlation into account. Instead, especially in more application-oriented research areas like medical imaging or robotics, data augmentation techniques that are based on heuristics a…
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Google Research（谷歌研究院）；Stanford University（斯坦福大学）；University College London（伦敦大学学院）；Google DeepMind（谷歌DeepMind）；Gatsby Computational Neuroscience Unit（加茨比计算神经科学单元）
Domain adaptation of neural networks commonly relies on three training phases: pretraining, selected data training and then fine tuning. Data selection improves target domain generalization by training further on pretraining data identified by relying on a small sample of target domain data. This work examines the benefit of data selection for language modeling and machine translation. Our experiments assess the complementarity of selection with fine tuning and result in practical recommendations: (i) selected data must be similar to the fine-tuning domain but not so much as to erode the compl…
Nanjing University of Aeronautics & Astronautics（南京航空航天大学）
Unsupervised Domain Adaptation (UDA) aims to classify unlabeled target domain by transferring knowledge from labeled source domain with domain shift. Most of the existing UDA methods try to mitigate the adverse impact induced by the shift via reducing domain discrepancy. However, such approaches easily suffer a notorious mode collapse issue due to the lack of labels in target domain. Naturally, one of the effective ways to mitigate this issue is to reliably estimate the pseudo labels for target domain, which itself is hard. To overcome this, we propose a novel UDA method named Progressive Adap…
Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation
State Key Laboratory for Multimedia Information Processing, School of Computer Science, PKU-Anker LLM Lab（多媒体信息处理国家重点实验室，计算机科学学院，PKU-Anker LLM实验室）；Peking University（北京大学）；University of California, Los Angeles（加州大学洛杉矶分校）；Paul G. Allen School of Computer Science（保罗·G·艾伦计算机科学学院）；University of Washington（华盛顿大学）；Hong Kong University of Science and Technology（香港科技大学）
Arizona State University（亚利桑那州立大学）；Army Research Lab（陆军研究实验室）
Studies show that the representations learned by deep neural networks can be transferred to similar prediction tasks in other domains for which we do not have enough labeled data. However, as we transition to higher layers in the model, the representations become more task-specific and less generalizable. Recent research on deep domain adaptation proposed to mitigate this problem by forcing the deep model to learn more transferable feature representations across domains. This is achieved by incorporating domain adaptation methods into deep learning pipeline. The majority of existing models lea…
Mila, Université de Montreal（蒙特利尔大学Mila研究所）；SAIT AI Lab, Montreal（蒙特利尔SAIT人工智能实验室）
Consider a collection of datasets generated by unknown interventions on an unknown structural causal model $G$. Recently, Bengio et al. (2020) conjectured that among all candidate models, $G$ is the fastest to adapt from one dataset to another, along with promising experiments. Indeed, intuitively $G$ has less mechanisms to adapt, but this justification is incomplete. Our contribution is a more thorough analysis of this hypothesis. We investigate the adaptation speed of cause-effect SCMs. Using convergence rates from stochastic optimization, we justify that a relevant proxy for adaptation spee…
Mila（米拉研究所）；Université de Montréal（蒙特利尔大学）；ETH Zurich（苏黎世联邦理工学院）；KTH Stockholm（斯德哥尔摩KTH皇家理工学院）；DeepMind（深度思维）
Learning models that offer robust out-of-distribution generalization and fast adaptation is a key challenge in modern machine learning. Modelling causal structure into neural networks holds the promise to accomplish robust zero and few-shot adaptation. Recent advances in differentiable causal discovery have proposed to factorize the data generating process into a set of modules, i.e. one module for the conditional distribution of every variable where only causal parents are used as predictors. Such a modular decomposition of knowledge enables adaptation to distributions shifts by only updating…
Consequential decision-making incentivizes individuals to strategically adapt their behavior to the specifics of the decision rule. While a long line of work has viewed strategic adaptation as gaming and attempted to mitigate its effects, recent work has instead sought to design classifiers that incentivize individuals to improve a desired quality. Key to both accounts is a cost function that dictates which adaptations are rational to undertake. In this work, we develop a causal framework for strategic adaptation. Our causal perspective clearly distinguishes between gaming and improvement and …
Selecting Robust Features for Machine Learning Applications using Multidata Causal Discovery
Institute of Earth Surface Dynamics（地球表面动力学研究所）；University of Lausanne（洛桑大学）；Institute of Data Science（数据科学研究所）；German Aerospace Center (DLR)（德国航空航天中心（DLR））；Faculty of Electrical Engineering and Computer Science（电气工程与计算机科学学院）；Technische Universität Berlin（柏林工业大学）
Domain adaptation (DA) arises as an important problem in statistical machine learning when the source data used to train a model is different from the target data used to test the model. Recent advances in DA have mainly been application-driven and have largely relied on the idea of a common subspace for source and target data. To understand the empirical successes and failures of DA methods, we propose a theoretical framework via structural causal models that enables analysis and comparison of the prediction performance of DA methods. This framework also allows us to itemize the assumptions n…
Causality Meets Locality: Provably Generalizable and Scalable Policy Learning for Networked Systems
King’s College London（伦敦国王学院）；Eindhoven University of Technology（埃因霍温理工大学）；University of California, San Diego（加州大学圣地亚哥分校）；The Alan Turing Institute（艾伦·图灵研究所）
Fairness of classification and regression has received much attention recently and various, partially non-compatible, criteria have been proposed. The fairness criteria can be enforced for a given classifier or, alternatively, the data can be adapated to ensure that every classifier trained on the data will adhere to desired fairness criteria. We present a practical data adaption method based on quantile preservation in causal structural equation models. The data adaptation is based on a presumed counterfactual model for the data. While the counterfactual model itself cannot be verified experi…
Université de Montréal（蒙特利尔大学）；Ruprecht-Karls-Universität Heidelberg（海德堡大学）；École Polytechnique Montréal（蒙特利尔综合理工学院）
We propose to meta-learn causal structures based on how fast a learner adapts to new distributions arising from sparse distributional changes, e.g. due to interventions, actions of agents and other sources of non-stationarities. We show that under this assumption, the correct causal structural choices lead to faster adaptation to modified distributions because the changes are concentrated in one or just a few mechanisms when the learned knowledge is modularized appropriately. This leads to sparse expected gradients and a lower effective number of degrees of freedom needing to be relearned whil…
New York University（纽约大学）；Genentech（基因泰克）；NYU Langone Health（纽约大学朗格尼医学中心）
Domain adaptation and covariate shift are big issues in deep learning and they ultimately affect any causal inference algorithms that rely on deep neural networks. Causal effect variational autoencoder (CEVAE) is trained to predict the outcome given observational treatment data and it suffers from the distribution shift at test time. In this paper, we introduce uniform treatment variational autoencoders (UTVAE) that are trained with uniform treatment distribution using importance sampling and show that using uniform treatment over observational treatment distribution leads to better causal inf…
Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；Univ. of Cambridge（剑桥大学）；Delft Univ. of Technology（代尔夫特理工大学）；Univ. of Copenhagen（哥本哈根大学）
Current methods for covariate-shift adaptation use unlabelled data to compute importance weights or domain-invariant features, while the final model is trained on labelled data only. Here, we consider a particular case of covariate shift which allows us also to learn from unlabelled data, that is, combining adaptation with semi-supervised learning. Using ideas from causality, we argue that this requires learning with both causes, $X_C$, and effects, $X_E$, of a target variable, $Y$, and show how this setting leads to what we call a semi-generative model, $P(Y,X_E|X_C,θ)$. Our approach is robus…
Confounding bias, missing data, and selection bias are three common obstacles to valid causal inference in the data sciences. Covariate adjustment is the most pervasive technique for recovering casual effects from confounding bias. In this paper, we introduce a covariate adjustment formulation for controlling confounding bias in the presence of missing-not-at-random data and develop a necessary and sufficient condition for recovering causal effects using the adjustment. We also introduce an adjustment formulation for controlling both confounding and selection biases in the presence of missing …
University of Helsinki（赫尔辛基大学）；HIIT（赫尔辛基信息技术研究所）；University of Oslo（奥斯陆大学）
We give methods for Bayesian inference of directed acyclic graphs, DAGs, and the induced causal effects from passively observed complete data. Our methods build on a recent Markov chain Monte Carlo scheme for learning Bayesian networks, which enables efficient approximate sampling from the graph posterior, provided that each node is assigned a small number $K$ of candidate parents. We present algorithmic techniques to significantly reduce the space and time requirements, which make the use of substantially larger values of $K$ feasible. Furthermore, we investigate the problem of selecting the …
Transferable Delay-Aware Reinforcement Learning via Implicit Causal Graph Modeling
College of Computer Science and Technology, National University of Defense Technology（计算机科学与技术学院，国防科技大学）；Intelligent Game and Decision Lab (IGDL)（智能游戏与决策实验室）；Institute of Military Transportation（军事交通运输研究院）；School of Artificial Intelligence, Hebei University of Technology（人工智能学院，河北工业大学）
