Causal discovery from observational data is challenging, especially with large datasets and complex relationships. Traditional methods often struggle with scalability and capturing global structural information. To overcome these limitations, we introduce a novel graph neural network (GNN)-based probabilistic framework that learns a probability distribution over the entire space of causal graphs, unlike methods that output a single deterministic graph. Our framework leverages a GNN that encodes both node and edge attributes into a unified graph representation, enabling the model to learn complex causal structures directly from data. The GNN model is trained on a diverse set of synthetic datasets augmented with statistical and information-theoretic measures, such as mutual information and conditional entropy, capturing both local and global data properties. We frame causal discovery as a supervised learning problem, directly predicting the entire graph structure. Our approach demonstrates superior performance, outperforming both traditional and recent non-GNN-based methods, as well as a GNN-based approach, in terms of accuracy and scalability on synthetic and real-world datasets without further training. This probabilistic framework significantly improves causal structure learning, with broad implications for decision-making and scientific discovery across various fields.
Many legal cases require decisions about causality, responsibility or blame, and these may be based on statistical data. However, causal inferences from such data are beset by subtle conceptual and practical difficulties, and in general it is, at best, possible to identify the "probability of causation" as lying between certain empirically informed limits. These limits can be refined and improved if we can obtain additional information, from statistical or scientific data, relating to the internal workings of the causal processes. In this paper we review and extend recent work in this area, wh…
The mainstream of research in genetics, epigenetics and imaging data analysis focuses on statistical association or exploring statistical dependence between variables. Despite their significant progresses in genetic research, understanding the etiology and mechanism of complex phenotypes remains elusive. Using association analysis as a major analytical platform for the complex data analysis is a key issue that hampers the theoretic development of genomic science and its application in practice. Causal inference is an essential component for the discovery of mechanical relationships among compl…
Functional data analysis, which handles data arising from curves, surfaces, volumes, manifolds and beyond in a variety of scientific fields, is a rapidly developing area in modern statistics and data science in the recent decades. The effect of a functional variable on an outcome is an essential theme in functional data analysis, but a majority of related studies are restricted to correlational effects rather than causal effects. This paper makes the first attempt to study the causal effect of a functional variable as a treatment in observational studies. Despite the lack of a probability dens…
We are interested in learning causal relationships between pairs of random variables, purely from observational data. To effectively address this task, the state-of-the-art relies on strong assumptions regarding the mechanisms mapping causes to effects, such as invertibility or the existence of additive noise, which only hold in limited situations. On the contrary, this short paper proposes to learn how to perform causal inference directly from data, and without the need of feature engineering. In particular, we pose causality as a kernel mean embedding classification problem, where inputs are…
There exist well-developed frameworks for causal modelling, but these require rather a lot of human domain expertise to define causal variables and perform interventions. In order to enable autonomous agents to learn abstract causal models through interactive experience, the existing theoretical foundations need to be extended and clarified. Existing frameworks give no guidance regarding variable choice / representation, and more importantly, give no indication as to which behaviour policies or physical transformations of state space shall count as interventions. The framework sketched in this…
Machine learning is the science of discovering statistical dependencies in data, and the use of those dependencies to perform predictions. During the last decade, machine learning has made spectacular progress, surpassing human performance in complex tasks such as object recognition, car driving, and computer gaming. However, the central role of prediction in machine learning avoids progress towards general-purpose artificial intelligence. As one way forward, we argue that causal inference is a fundamental component of human intelligence, yet ignored by learning algorithms. Causal inference is…
University of Cambridge（剑桥大学）；University of Cagliari（卡利亚里大学）
Given empirical evidence for the dependence of an outcome variable on an exposure variable, we can typically only provide bounds for the "probability of causation" in the case of an individual who has developed the outcome after being exposed. We show how these bounds can be adapted or improved if further information becomes available. In addition to reviewing existing work on this topic, we provide a new analysis for the case where a mediating variable can be observed. In particular we show how the probability of causation can be bounded when there is no direct effect and no confounding. Keyw…
An important goal across most scientific fields is the discovery of causal structures underling a set of observations. Unfortunately, causal discovery methods which are based on correlation or mutual information can often fail to identify causal links in systems which exhibit dynamic relationships. Such dynamic systems (including the famous coupled logistic map) exhibit `mirage' correlations which appear and disappear depending on the observation window. This means not only that correlation is not causation but, perhaps counter-intuitively, that causation may occur without correlation. In this…
On Learning Necessary and Sufficient Causal Graphs
University of California, Irvine（加州大学尔湾分校）；University of Michigan（密歇根大学）；University of California, Berkeley（加州大学伯克利分校）；North Carolina State University（北卡罗来纳州立大学）
Causal processes in biomedicine may contain cycles, evolve over time or differ between populations. However, many graphical models cannot accommodate these conditions. We propose to model causation using a mixture of directed cyclic graphs (DAGs), where the joint distribution in a population follows a DAG at any single point in time but potentially different DAGs across time. We also introduce an algorithm called Causal Inference over Mixtures that uses longitudinal data to infer a graph summarizing the causal relations generated from a mixture of DAGs. Experiments demonstrate improved perform…
Northeastern University（东北大学）；GE Global Research（通用电气全球研究中心）
Recent developments have linked causal inference with Algorithmic Information Theory, and methods have been developed that utilize Conditional Kolmogorov Complexity to determine causation between two random variables. We present a method for inferring causal direction between continuous variables by using an MDL Binning technique for data discretization and complexity calculation. Our method captures the shape of the data and uses it to determine which variable has more information about the other. Its high predictive performance and robustness is shown on several real world use cases.
Causality can be described in terms of a structural causal model (SCM) that carries information on the variables of interest and their mechanistic relations. For most processes of interest the underlying SCM will only be partially observable, thus causal inference tries leveraging the exposed. Graph neural networks (GNN) as universal approximators on structured input pose a viable candidate for causal learning, suggesting a tighter integration with SCM. To this effect we present a theoretical analysis from first principles that establishes a more general view on neural-causal models, revealing…
ISI Foundation（ISI基金会）；UniCredit（裕信银行）；New York University（纽约大学）；Stanford University（斯坦福大学）
Mastering the dynamics of social influence requires separating, in a database of information propagation traces, the genuine causal processes from temporal correlation, i.e., homophily and other spurious causes. However, most studies to characterize social influence, and, in general, most data-science analyses focus on correlations, statistical independence, or conditional independence. Only recently, there has been a resurgence of interest in "causal data science", e.g., grounded on causality theories. In this paper we adopt a principled causal approach to the analysis of social influence fro…
Identifying the effects of causes and causes of effects is vital in virtually every scientific field. Often, however, the needed probabilities may not be fully identifiable from the data sources available. This paper shows how partial identifiability is still possible for several probabilities of causation. We term this epsilon-identifiability and demonstrate its usefulness in cases where the behavior of certain subpopulations can be restricted to within some narrow bounds. In particular, we show how unidentifiable causal effects and counterfactual probabilities can be narrowly bounded when su…
Most algorithms in classical and contemporary machine learning focus on correlation-based dependence between features to drive performance. Although success has been observed in many relevant problems, these algorithms fail when the underlying causality is inconsistent with the assumed relations. We propose a novel model-agnostic loss function called Causal Loss that improves the interventional quality of the prediction using an intervened neural-causal regularizer. In support of our theoretical results, our experimental illustration shows how causal loss bestows a non-causal associative model…
Harvard T.H. Chan School of Public Health（哈佛大学陈曾熙公共卫生学院）
Genome-wide association studies (GWAS) have emerged as a rich source of genetic clues into disease biology, and they have revealed strong genetic correlations among many diseases and traits. Some of these genetic correlations may reflect causal relationships. We developed a method to quantify causal relationships between genetically correlated traits using GWAS summary association statistics. In particular, our method quantifies what part of the genetic component of trait 1 is also causal for trait 2 using mixed fourth moments $E(α_1^2α_1α_2)$ and $E(α_2^2α_1α_2)$ of the bivariate effect size …
Information theory gives rise to a novel method for causal skeleton discovery by expressing associations between variables as tensors. This tensor-based approach reduces the dimensionality of the data needed to test for conditional independence, e.g., for systems comprising three variables, the causal skeleton can be determined using pair-wise determined tensors. To arrive at this result, an additional information measure, path information, is proposed.
Probabilities of Causation and Root Cause Analysis with Quasi-Markovian Models
institutetext: 1 Instituto de Ciência e Tecnologia Itaú (ICTi) 2 Escola Politécnica, Universidade de São Paulo (EP-USP) 3 Instituto de Matemática e Estatística, Universidade de São Paulo (IME-USP)（巴西圣保罗大学信息技术研究所（ICTi）、圣保罗大学工学院（EP-USP）、圣保罗大学数学与统计学研究所（IME-USP））；Instituto de Ciência e Tecnologia Itaú (ICTi) 2 Escola Politécnica, Universidade de São Paulo (EP-USP) 3 Instituto de Matemática e Estatística, Universidade de São Paulo (IME-USP)（巴西圣保罗大学信息技术研究所（ICTi）、圣保罗大学工学院（EP-USP）、圣保罗大学数学与统计学研究所（IME-USP））
Robot Pouring: Identifying Causes of Spillage and Selecting Alternative Action Parameters Using Probabilistic Actual Causation
Cognitive Neuroinformatics University of Bremen（认知神经信息学不列颠群岛大学）；University of Bremen（不列颠群岛大学）；Leibniz Institute for Prevention Research and Epidemiology - BIPS（莱比锡预防研究与流行病学研究所 - BIPS）
Impact, Causation and Prediction of Socio-Academic and Economic Factors in Exam-centric Student Evaluation Measures using Machine Learning and Causal Analysis
Institute of Information Technology（信息科技研究所）；Jahangirnagar University（贾汗吉尔纳加尔大学）；University of Maryland Baltimore County（马里兰大学巴尔的摩县分校）；Erik Jonsson School of Engineering and Computer Science（埃里克·乔纳森工程与计算机科学学院）；University of Texas（德克萨斯大学）
Can Large Language Models Infer Causation from Correlation?
Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；ETH Zürich（苏黎世联邦理工学院）；LTI, CMU（卡内基梅隆大学语言技术研究所）；University of Hong Kong（香港大学）；Meta AI；University of Michigan（密歇根大学）
The Hong Kong Polytechnic University（香港理工大学）；University of Toronto（多伦多大学）
This paper presents Gem, a model-agnostic approach for providing interpretable explanations for any GNNs on various graph learning tasks. Specifically, we formulate the problem of providing explanations for the decisions of GNNs as a causal learning task. Then we train a causal explanation model equipped with a loss function based on Granger causality. Different from existing explainers for GNNs, Gem explains GNNs on graph-structured data from a causal perspective. It has better generalization ability as it has no requirements on the internal structure of the GNNs or prior knowledge on the gra…
Incorporating Retrieval-based Causal Learning with Information Bottlenecks for Interpretable Graph Neural Networks
School of Computer Science and Engineering, Sun Yat-sen University（中山大学计算机科学与工程学院）；Shanghai Jiao Tong University（上海交通大学）；Key Laboratory of Machine Intelligence and Advanced Computing, Sun Yat-sen University（中山大学机器智能与先进计算重点实验室）
From Correlation to Causation: Max-Pooling-Based Multi-Instance Learning Leads to More Robust Whole Slide Image Classification
UniSA STEM, University of South Australia（南澳大利亚大学STEM学院）；School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）；Key Lab. of Computer Network and Information Integration (Southeast University), MoE, China（计算机网络与信息集成重点实验室（东南大学），教育部，中国）；School of Information and Physical Sciences, The University of Newcastle（新castle大学信息与物理科学学院）；Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)（穆罕默德·本·扎耶德人工智能大学）
Rethinking Causal Relationships Learning in Graph Neural Networks
Science and Technology on Integrated Information System Laboratory, Institute of Software Chinese Academy of Sciences（中国科学院软件研究所综合信息系统技术国家级重点实验室）；University of Chinese Academy of Sciences（中国科学院大学）；State Key Laboratory of Intelligent Game（智能博弈国家重点实验室）；Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）
Deciphering Spatio-Temporal Graph Forecasting: A Causal Lens and Treatment
National University of Singapore（新加坡国立大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Beijing Jiaotong University（北京交通大学）；University of Science and Technology of China（中国科学技术大学）
A novel approach to the relationships between data features -- based on comprehensive examination of mathematical, technological, and causal methodology
Legal Research Institute of Korea University（高丽大学法律研究所）；Human-Inspired AI Research, Korea University（高丽大学类人人工智能研究院）
Huazhong University of Science and Technology（华中科技大学）；Beijing Jiaotong University（北京交通大学）
As a prevalent problem in online advertising, CTR prediction has attracted plentiful attention from both academia and industry. Recent studies have been reported to establish CTR prediction models in the graph neural networks (GNNs) framework. However, most of GNNs-based models handle feature interactions in a complete graph, while ignoring causal relationships among features, which results in a huge drop in the performance on out-of-distribution data. This paper is dedicated to developing a causality-based CTR prediction model in the GNNs framework (Causal-GNN) integrating representations of …
LLM Enhancers for GNNs: An Analysis from the Perspective of Causal Mechanism Identification
Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所）；National Key Laboratory of Space Integrated Information System（空间信息集成系统国家重点实验室）；University of Chinese Academy of Sciences（中国科学院大学）；Tsinghua University（清华大学）
University of Maryland Baltimore County（马里兰大学巴尔的摩郡分校）；Univerisy of Alabama（阿拉巴马大学）
In recent years, there is a lot of interest in modeling students' digital traces in Learning Management System (LMS) to understand students' learning behavior patterns including aspects of meta-cognition and self-regulation, with the ultimate goal to turn those insights into actionable information to support students to improve their learning outcomes. In achieving this goal, however, there are two main issues that need to be addressed given the existing literature. Firstly, most of the current work is course-centered (i.e. models are built from data for a specific course) rather than student-…
Beijing University of Posts and Telecommunications（北京邮电大学）；Mila - Québec AI Institute（米拉-魁北克人工智能研究所）；HEC Montréal（蒙特利尔高等商学院）
Most Graph Neural Networks (GNNs) predict the labels of unseen graphs by learning the correlation between the input graphs and labels. However, by presenting a graph classification investigation on the training graphs with severe bias, surprisingly, we discover that GNNs always tend to explore the spurious correlations to make decision, even if the causal correlation always exists. This implies that existing GNNs trained on such biased datasets will suffer from poor generalization capability. By analyzing this problem in a causal view, we find that disentangling and decorrelating the causal an…
