Identifying variables responsible for changes to a biological system enables applications in drug target discovery and cell engineering. Given a pair of observational and interventional datasets, the goal is to isolate the subset of observed variables that were the targets of the intervention. Directly applying causal discovery algorithms is challenging: the data may contain thousands of variables with as few as tens of samples per intervention, and biological systems do not adhere to classical causality assumptions. We propose a causality-inspired approach to address this practical setting. First, we infer noisy causal graphs from the observational and interventional data. Then, we learn to map the differences between these graphs, along with additional statistical features, to sets of variables that were intervened upon. Both modules are jointly trained in a supervised framework, on simulated and real data that reflect the nature of biological interventions. This approach consistently outperforms baselines for perturbation modeling on seven single-cell transcriptomics datasets. We also demonstrate significant improvements over current causal discovery methods for predicting soft and hard intervention targets across a variety of synthetic data.
Pacific Northwest National Laboratory（太平洋西北国家实验室）；Microsoft（微软）；Northeastern University（东北大学）；Enveda Biosciences（恩维达生物科学公司）；Altdeep（阿尔特迪普）
Counterfactual inference is a useful tool for comparing outcomes of interventions on complex systems. It requires us to represent the system in form of a structural causal model, complete with a causal diagram, probabilistic assumptions on exogenous variables, and functional assignments. Specifying such models can be extremely difficult in practice. The process requires substantial domain expertise, and does not scale easily to large systems, multiple systems, or novel system modifications. At the same time, many application domains, such as molecular biology, are rich in structured causal kno…
Efficient Symbolic Computations for Identifying Causal Effects
Max Planck Institute for Mathematics in the Sciences（马克斯·普朗克数学研究院）；Binghamton University（宾夕法尼亚州立大学布林茅尔分校）；École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）
Responsible AI Research Centre（负责任人工智能研究中心）；Australian Institute for Machine Learning（澳大利亚机器学习研究所）；School of Computer Science and Engineering（计算机科学与工程学院）；School of Mathematics and Statistics（数学与统计学学院）；Halicioğlu Data Science Institute（Halicioğlu数据科学研究所）；Department of Philosophy（哲学系）
GO-Based Clustering for Learning Cluster-Level Causal Gene Regulatory Networks
L3S Research Center, Leibniz University Hannover（莱布尼茨汉诺威大学L3S研究中心）；Lower Saxony Center for Artificial Intelligence and Causal Methods in Medicine (CAIMed)（下萨克森州人工智能与因果医学方法中心（CAIMed））；Institute for Causal and Explainable Artificial Intelligence in Life Sciences（生命科学因果与可解释人工智能研究所）；Center for Computational Life Sciences（计算生命科学中心）；RWTH Aachen University（亚琛工业大学）
iSCAN: Identifying Causal Mechanism Shifts among Nonlinear Additive Noise Models
University of Chicago（芝加哥大学）；Booth School of Business, University of Chicago（芝加哥大学布斯商学院）；Carnegie Mellon University（卡内基梅隆大学）；University of Texas at Austin（德克萨斯大学奥斯汀分校）
Institute of Computational Biology, Helmholtz Munich（亥姆霍兹慕尼黑计算生物学研究所）；Mila, the Quebec AI Institute（米拉魁北克人工智能研究所）；Université de Montréal（蒙特利尔大学）；Helmholtz AI（亥姆霍兹人工智能研究所）；Technical University of Munich（慕尼黑工业大学）；Sanger Institute（桑格研究所）
SRI International（SRI国际）；George Washington Univ.（乔治华盛顿大学）；GE Global Research（通用电气全球研究中心）
There is an abundance of complex dynamic systems that are critical to our daily lives and our society but that are hardly understood, and even with today's possibilities to sense and collect large amounts of experimental data, they are so complex and continuously evolving that it is unlikely that their dynamics will ever be understood in full detail. Nevertheless, through computational tools we can try to make the best possible use of the current technologies and available data. We believe that the most useful models will have to take into account the imbalance between system complexity and av…
University of Valencia（瓦伦西亚大学）；University of Cambridge（剑桥大学）；Institut Pierre-Simon Laplace（皮埃尔-西蒙·拉普拉斯研究所）；Columbia University（哥伦比亚大学）；University of Oxford（牛津大学）；Pasteur Labs（巴斯德实验室）
A first causal discovery analysis from observational data of pyroCb (storm clouds generated from extreme wildfires) is presented. Invariant Causal Prediction was used to develop tools to understand the causal drivers of pyroCb formation. This includes a conditional independence test for testing $Y$ conditionally independent of $E$ given $X$ for binary variable $Y$ and multivariate, continuous variables $X$ and $E$, and a greedy-ICP search algorithm that relies on fewer conditional independence tests to obtain a smaller more manageable set of causal predictors. With these tools, we identified a…
The Graduate University for Advanced Studies（综合研究大学院大学）；The Institute of Statistical Mathematics（统计数学研究所）
As an important problem in causal inference, we discuss the identification and estimation of treatment effects (TEs) under limited overlap; that is, when subjects with certain features belong to a single treatment group. We use a latent variable to model a prognostic score which is widely used in biostatistics and sufficient for TEs; i.e., we build a generative prognostic model. We prove that the latent variable recovers a prognostic score, and the model identifies individualized treatment effects. The model is then learned as β-Intact-VAE--a new type of variational autoencoder (VAE). We deriv…
Identifying causal relationships for a treatment intervention is a fundamental problem in health sciences. Randomized controlled trials (RCTs) are considered the gold standard for identifying causal relationships. However, recent advancements in the theory of causal inference based on the foundations of structural causal models (SCMs) have allowed the identification of causal relationships from observational data, under certain assumptions. Survival analysis provides standard measures, such as the hazard ratio, to quantify the effects of an intervention. While hazard ratios are widely used in …
Score-Based Causal Discovery of Latent Variable Causal Models
Carnegie Mellon University（卡内基梅隆大学）；University of California, San Diego（加州大学圣地亚哥分校）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
Causal Discovery methods aim to identify a DAG structure that represents causal relationships from observational data. In this article, we stress that it is important to test such methods for robustness in practical settings. As our main example, we analyze the NOTEARS method, for which we demonstrate a lack of scale-invariance. We show that NOTEARS is a method that aims to identify a parsimonious DAG from the data that explains the residual variance. We conclude that NOTEARS is not suitable for identifying truly causal relationships from the data.
Interpretable Causal Representation Learning for Biological Data in the Pathway Space
CIMA University of Navarra, CCUN, IdiSNA（纳瓦拉大学CIMA、CCUN、IdiSNA）；TECNUN, University of Navarra, San Sebastián（纳瓦拉大学TECNUN、桑塞巴斯蒂安）；Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST)（科学技术大学（KAUST）生物与环境科学与工程部门）；Dept. of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic（梅奥诊所分子药理学与实验治疗学部门）；Institute of Chemical Biology, Ilia State University（伊利亚州立大学化学生物学研究所）；Center for Data Science (DATAI), University of Navarra（纳瓦拉大学数据科学中心（DATAI））
Stanford University（斯坦福大学）；University of Milan-Bicocca（米兰比可卡大学）；University of Edinburgh（爱丁堡大学）
Several diseases related to cell proliferation are characterized by the accumulation of somatic DNA changes, with respect to wildtype conditions. Cancer and HIV are two common examples of such diseases, where the mutational load in the cancerous/viral population increases over time. In these cases, selective pressures are often observed along with competition, cooperation and parasitism among distinct cellular clones. Recently, we presented a mathematical framework to model these phenomena, based on a combination of Bayesian inference and Suppes' theory of probabilistic causation, depicted in …
A significant challenge in reinforcement learning is quantifying the complex relationship between actions and long-term rewards. The effects may manifest themselves over a long sequence of state-action pairs, making them hard to pinpoint. In this paper, we propose a method to link transitions with significant deviations in state with unusually large variations in subsequent rewards. Such transitions are marked as possible causal effects, and the corresponding state-action pairs are added to a separate replay buffer. In addition, we include \textit{contrastive} samples corresponding to transiti…
We propose an approach for learning the causal structure in stochastic dynamical systems with a $1$-step functional dependency in the presence of latent variables. We propose an information-theoretic approach that allows us to recover the causal relations among the observed variables as long as the latent variables evolve without exogenous noise. We further propose an efficient learning method based on linear regression for the special sub-case when the dynamics are restricted to be linear. We validate the performance of our approach via numerical simulations.
Peking University（北京大学）；Renmin University of China（中国人民大学）；Beijing Technology and Business University（北京工商大学）；Huawei Noah’s Ark Lab（华为诺亚方舟实验室）
Causality is important for designing interpretable and robust methods in artificial intelligence research. We propose a local approach to identify whether a variable is a cause of a given target under the framework of causal graphical models of directed acyclic graphs (DAGs). In general, the causal relation between two variables may not be identifiable from observational data as many causal DAGs encoding different causal relations are Markov equivalent. In this paper, we first introduce a sufficient and necessary graphical condition to check the existence of a causal path from a variable to a …
Practical Causal Evaluation Metrics for Biological Networks
The University of Tokyo（东京大学）；Istituto Dalle Molle di Studi sull'Intelligenza Artificiale (IDSIA)（达尔·莫尔人工智能研究所）；Kyushu University（九州大学）；Aichi Cancer Center Research Institute（爱知县癌症中心研究所）；Nagoya University（名古屋大学）
MapPFN: Learning Causal Perturbation Maps in Context
Machine Learning Group, Technische Universität Berlin（技术大学柏林机器学习组）；Berlin Institute for the Foundations of Learning and Data (BIFOLD)（柏林学习与数据基础研究院（BIFOLD））；Aignostics, Berlin, Germany（柏林Aignostics公司）；Institute of Pathology, Charité - Universitätsmedizin Berlin（柏林Charité大学医学中心病理研究所）
Recommending the best course of action for an individual is a major application of individual-level causal effect estimation. This application is often needed in safety-critical domains such as healthcare, where estimating and communicating uncertainty to decision-makers is crucial. We introduce a practical approach for integrating uncertainty estimation into a class of state-of-the-art neural network methods used for individual-level causal estimates. We show that our methods enable us to deal gracefully with situations of "no-overlap", common in high-dimensional data, where standard applicat…
Uppsala University（乌普萨拉大学）；RWTH Aachen University（亚琛工业大学）；KTH Royal Institute of Technology（瑞典皇家理工学院）
Mathematical models are fundamental building blocks in the design of dynamical control systems. As control systems are becoming increasingly complex and networked, approaches for obtaining such models based on first principles reach their limits. Data-driven methods provide an alternative. However, without structural knowledge, these methods are prone to finding spurious correlations in the training data, which can hamper generalization capabilities of the obtained models. This can significantly lower control and prediction performance when the system is exposed to unknown situations. A preced…
Machine Learning explainability techniques have been proposed as a means of `explaining' or interrogating a model in order to understand why a particular decision or prediction has been made. Such an ability is especially important at a time when machine learning is being used to automate decision processes which concern sensitive factors and legal outcomes. Indeed, it is even a requirement according to EU law. Furthermore, researchers concerned with imposing overly restrictive functional form (e.g., as would be the case in a linear regression) may be motivated to use machine learning algorith…
Paris-Saclay University（巴黎-萨克雷大学）；UVSQ（凡尔赛大学）；Inserm（法国国家健康与医学研究院）；Gustave Roussy（古斯塔夫·鲁西研究所）；Epiconcept Company（Epiconcept公司）；Accenture Labs（埃森哲实验室）；Sorbonne University（索邦大学）；University Sorbonne Paris Nord（索邦巴黎北大学）；Indian Institute of Science（印度科学学院）；Luxembourg Institute of Health（卢森堡健康研究院）
Objective: Leveraging machine learning methods, we aim to extract both explicit and implicit cause-effect associations in patient-reported, diabetes-related tweets and provide a tool to better understand opinion, feelings and observations shared within the diabetes online community from a causality perspective. Materials and Methods: More than 30 million diabetes-related tweets in English were collected between April 2017 and January 2021. Deep learning and natural language processing methods were applied to focus on tweets with personal and emotional content. A cause-effect-tweet dataset was …
Indraprastha Institute of Information Technology（印多尔帕拉斯塔信息技术学院）；Delhi Technological University（德里理工大学）
Structure learning offers an expressive, versatile and explainable approach to causal and mechanistic modeling of complex biological data. We present wiseR, an open source application for learning, evaluating and deploying robust causal graphical models using graph neural networks and Bayesian networks. We demonstrate the utility of this application through application on for biomarker discovery in a COVID-19 clinical dataset.
DZNE（德国神经退行性疾病中心）；RWTH Aachen University（亚琛工业大学）；University of Oxford（牛津大学）；University of Cambridge（剑桥大学）
Recent years have seen rapid progress at the intersection between causality and machine learning. Motivated by scientific applications involving high-dimensional data, in particular in biomedicine, we propose a deep neural architecture for learning causal relationships between variables from a combination of empirical data and prior causal knowledge. We combine convolutional and graph neural networks within a causal risk framework to provide a flexible and scalable approach. Empirical results include linear and nonlinear simulations (where the underlying causal structures are known and can be …
ODBAE: a high-performance model identifying complex phenotypes in high-dimensional biological datasets
School of Mathematical Sciences, Soochow University（苏州大学数学科学学院）；Center for Systems Biology, Soochow University（苏州大学系统生物学研究中心）；Jiangsu Province Engineering Research Center of Development and Translation of Key Technologies for Chronic Disease Prevention and Control, Suzhou Vocational Health College（苏州卫生职业技术学院江苏省慢性疾病防控关键技术开发与转化工程研究中心）；Cambridge-Suda Genomic Research Center, Suzhou medical college of Soochow University（苏州大学医学部剑桥-苏大基因组资源中心）；KTL Research Acceleration LTD（KTL研究加速有限公司）；Jiangsu Key Laboratory of Neuropsychiatric Diseases, Suzhou medical college of Soochow University（苏州大学医学部江苏省神经精神疾病重点实验室）
University of Texas at Austin（德克萨斯大学奥斯汀分校）；University of California, Berkeley（加利福尼亚大学伯克利分校）
We study the problem of best arm identification with a fairness constraint in a given causal model. The goal is to find a soft intervention on a given node to maximize the outcome while meeting a fairness constraint by counterfactual estimation with only partial knowledge of the causal model. The problem is motivated by ensuring fairness on an online marketplace. We provide theoretical guarantees on the probability of error and empirically examine the effectiveness of our algorithm with a two-stage baseline.
Department of Statistics and Data Science, Northwestern University（统计与数据科学系，西北大学）；Department of Biomedical Informatics, Harvard University（生物医学信息学系，哈佛大学）；Department of Computer Science, University of Illinois Chicago（计算机科学系，伊利诺伊大学芝加哥分校）
