Causal Mining Research Articles

Causal V: A five-phase causal mining methodology for real-world accident data

Causal V: A five-phase causal mining methodology for real-world accident data

Mining Delay Propagation Causality within an Airport Network from Historical Data

Airport networks are interconnected through flight routes, with delays at upstream airports leading to delays at downstream airports, thus causing delay propagation. Exploring the mechanisms of delay propagation in airport networks provides scientific insights for managing and controlling delays in aviation systems. Existing methods, such as Granger causality tests and transfer entropy, must be revised to address the nonlinear causal relationships of delays in airport networks. So, this paper proposes a causality mining method for delay propagation in airport networks based on partial correlation-based multivariate conditional independence (PCMCI). This method comprehensively considers all airports and causality mining in two stages. The first stage uses conditional independence tests to obtain the parent node set of the target airport, which includes both true and false causal relationships. The second stage employs instantaneous conditional independence tests to eliminate false causal relationships and obtain test statistics representing the strength of causality. Based on historical delay data from US airports over a year, the experimental results show that multiple factors cause delay propagation in airport networks rather than a single causal relationship. The scope of delay propagation is limited, mainly affecting a few airports closely connected to it. Delays at airports with small flight volumes are more likely to propagate. Few airport pairs in the network mutually propagate delays and, often, delays at airports affected by a particular airport’s delay also exhibit causal relationships with each other. This method provides a new perspective for deepening the understanding of delay propagation mechanisms in airport networks.

Causal Disentanglement: A Generalized Bearing Fault Diagnostic Framework in Continuous Degradation Mode.

In recent years, the identification of out-of-distribution faults has become a hot topic in the field of intelligent diagnosis. Existing researches usually adopt domain adaptation methods to complete the generalization of diagnostic knowledge with the aid of target domain data, but the acquisition of fault samples in real industries is extremely time-consuming and costly. Moreover, most researches focus on samples with fixed fault levels, ignoring the fact that system degradation is a continuous process. In response to the above intractable problems, this article proposed a causal disentanglement network (CDN) to realize cross-machine knowledge generalization and continuous degradation mode diagnosis. In CDN, multitask instance normalization and batch normalization structure was proposed to learn task-specific knowledge and enhance the informativeness of the extracted features. On this basis, a causal disentanglement loss was proposed, which minimized the mutual information of features between subtask structures and captured the causal invariant fault information for better generalization. The experimental results proved the superiority and generalization ability of CDN, and the visualization results proved the performance of CDN in causality mining.

Neural Network Weight Comparison for Industrial Causality Discovering and Its Soft Sensing Application

Due to the complex reaction mechanisms of industrial process units, causality and correlations exist between industrial process variables. Causal discovery algorithms have been utilized to discover the knowledge on variable relationships and guide process modeling and control optimization. However, most of them are limited by strict assumptions, such as linear relationships, additive noise, steady-state process, etc. Therefore, these methods cannot gain good performance for most practical industrial processes. To solve these problems, a novel weight comparison causal mining (WCCM) algorithm is proposed in this paper for industrial causal graph discovery. It first trains a group of hidden layer neural networks with process data, then mines an undirected skeleton of the process variables according to the comparison of the network weights, and further determines the causal directions of the undirected edges in the skeleton to get a directed causal graph. The effectiveness of WCCM is verified on a benchmark and a practical industrial case from the Urea Synthesis process. The undirected and direct edges mined by WCCM show high consistency with the ground truths. Moreover, the causal discovery results of WCCM is utilized to guide the feature selection of soft sensor modeling, resulting in improved prediction accuracy and enhanced model interpretability.

Causal discovery approach with reinforcement learning for risk factors of type II diabetes mellitus

BackgroundStatistical correlation analysis is currently the most typically used approach for investigating the risk factors of type 2 diabetes mellitus (T2DM). However, this approach does not readily reveal the causal relationships between risk factors and rarely describes the causal relationships visually.ResultsConsidering the superiority of reinforcement learning in prediction, a causal discovery approach with reinforcement learning for T2DM risk factors is proposed herein. First, a reinforcement learning model is constructed for T2DM risk factors. Second, the process involved in the causal discovery method for T2DM risk factors is detailed. Finally, several experiments are designed based on diabetes datasets and used to verify the proposed approach.ConclusionsThe experimental results show that the proposed approach improves the accuracy of causality mining between T2DM risk factors and provides new evidence to researchers engaged in T2DM prevention and treatment research.

Toward a Multi-Column Knowledge-Oriented Neural Network for Web Corpus Causality Mining

In the digital age, many sources of textual content are devoted to studying and expressing many sorts of relationships, including employer–employee, if–then, part–whole, product–producer, and cause–effect relations/causality. Mining cause–effect relations are a key topic in many NLP (natural language processing) applications, such as future event prediction, information retrieval, healthcare, scenario generation, decision making, commerce risk management, question answering, and adverse drug reaction. Many statistical and non-statistical methods have been developed in the past to address this topic. Most of them frequently used feature-driven supervised approaches and hand-crafted linguistic patterns. However, the implicit and ambiguous statement of causation prevented these methods from achieving great recall and precision. They cover a limited set of implicit causality and are difficult to extend. In this work, a novel MCKN (multi-column knowledge-oriented network) is introduced. This model includes various knowledge-oriented channels/columns (KCs), where each channel integrates prior human knowledge to capture language cues of causation. MCKN uses unique convolutional word filters (wf) generated automatically using WordNet and FrameNet. To reduce MCKN’s dimensionality, we use filter selection and clustering approaches. Our model delivers superior performance on the Alternative Lexicalization (AltLexes) dataset, proving that MCKN is a simpler and distinctive approach for informal datasets.

Lung nodule detection algorithm based on rank correlation causal structure learning

Early diagnosis can significantly improve the survival rate of lung cancer patients. This study attempts to construct a causal structure network between the computational and semantic features of lung nodules through causal discovery algorithms, and to detect and prevent lung nodules based on this network. For complex and diverse lung nodule data sets, this paper proposes a new causal lung nodule detection algorithm Tau-CSFS in combination with rank correlation methods. The algorithm can effectively mine the causal relationship among lung cancer data that obey the non-linear non-Gaussian distribution, and the mixture of continuous and discrete variables, and has good predictive performance. We made three main contributions. First, we proved that the Kendall rank correlation coefficient that does not require data distribution can be used as a standard for independence test. Second, we applied Kendall rank correlation to Bayesian structure learning, and proposed a new causal discovery algorithm: Tau-CS algorithm based on hypothesis testing. The third contribution is to combine the Tau-CS algorithm with the feature selection method, and further propose the Tau-CSFS algorithm, which solves the problem of causality mining and diagnosis detection of lung nodule data. In the experiment, the Tau-CS algorithm is compared with the prior art on 7 Bayesian networks on the additive noise structure model, and it is proved that the algorithm has a better accuracy of causal structure learning. Finally, in the lung nodule detection stage, using the processed LIDC data set to perform two-classification and multi-classification experiments on seven semantic categories, the average accuracy of the Tau-CSFS algorithm reached 85.84% and 83.32%. The Tau-CSFS algorithm are better than comparable similar algorithms in the comprehensive performance index. The results show that the proposed algorithm has good detection performance and wide application prospects.

Towards Improving Causality Mining using BERT with Multi-level Feature Networks

Towards Improving Causality Mining using BERT with Multi-level Feature Networks

Whitening-Net: A Generalized Network to Diagnose the Faults Among Different Machines and Conditions.

Intelligent bearing diagnostic methods are developing rapidly, but they are difficult to implement due to the lack of real industrial data. A feasible way to deal with this problem is to train a network through laboratory data to mine the causality of bearing faults. This means that the constructed network can handle domain deviations caused by the change of machines, working conditions, noise, and so on which is, however, not a simple task. In response to this problem, a new domain generalization framework-Whitening-Net-was proposed in this article. This framework first defined the homologous compound domain signal as the data basis. Subsequently, the causal loss was proposed to impose regularization constraints on the network, which enhances the network's ability to mine causality. To avoid domain-specific information from interfering with causal mining, a whitening structure was proposed to whiten the domain, prompting the network to pay more attention to the causality of the signal rather than the domain noise. The results of diagnosis and interpretation proved the ability of Whitening-Net in mining causal mechanisms, which shows that the proposed network can generalize to different machines, even if the tested working conditions and bearing types are completely different from the training domains.

DIC-ST: A Hybrid Prediction Framework Based on Causal Structure Learning for Cellular Traffic and Its Application in Urban Computing

The development of technology has strongly affected regional urbanization. With development of mobile communication technology, intelligent devices have become increasingly widely used in people’s lives. The application of big data in urban computing is multidimensional; it has been involved in different fields, such as urban planning, network optimization, intelligent transportation, energy consumption and so on. Data analysis becomes particularly important for wireless networks. In this paper, a method for analyzing cellular traffic data was proposed. Firstly, a method to extract trend components, periodic components and essential components from complex traffic time series was proposed. Secondly, we introduced causality data mining. Different from traditional time series causality analysis, the depth of causal mining was increased. We conducted causality verification on different components of time series and the results showed that the causal relationship between base stations is different in trend component, periodic component and essential component in urban wireless network. This is crucial for urban planning and network management. Thirdly, DIC-ST: a spatial temporal time series prediction based on decomposition and integration system with causal structure learning was proposed by combining GCN. Final results showed that the proposed method significantly improves the accuracy of cellular traffic prediction. At the same time, this method can play a crucial role for urban computing in network management, intelligent transportation, base station siting and energy consumption when combined with remote sensing map information.

Causality Mining in Natural Languages Using Machine and Deep Learning Techniques: A Survey

The era of big textual corpora and machine learning technologies have paved the way for researchers in numerous data mining fields. Among them, causality mining (CM) from textual data has become a significant area of concern and has more attention from researchers. Causality (cause-effect relations) serves as an essential category of relationships, which plays a significant role in question answering, future events predication, discourse comprehension, decision making, future scenario generation, medical text mining, behavior prediction, and textual prediction entailment. While, decades of development techniques for CM are still prone to performance enhancement, especially for ambiguous and implicitly expressed causalities. The ineffectiveness of the early attempts is mainly due to small, ambiguous, heterogeneous, and domain-specific datasets constructed by manually linguistic and syntactic rules. Many researchers have deployed shallow machine learning (ML) and deep learning (DL) techniques to deal with such datasets, and they achieved satisfactory performance. In this survey, an effort has been made to address a comprehensive review of some state-of-the-art shallow ML and DL approaches in CM. We present a detailed taxonomy of CM and discuss popular ML and DL approaches with their comparative weaknesses and strengths, applications, popular datasets, and frameworks. Lastly, the future research challenges are discussed with illustrations of how to transform them into productive future research directions.

A practical approach towards causality mining in clinical text using active transfer learning

ObjectiveCausality mining is an active research area, which requires the application of state-of-the-art natural language processing techniques. In the healthcare domain, medical experts create clinical text to overcome the limitation of well-defined and schema driven information systems. The objective of this research work is to create a framework, which can convert clinical text into causal knowledge. MethodsA practical approach based on term expansion, phrase generation, BERT based phrase embedding and semantic matching, semantic enrichment, expert verification, and model evolution has been used to construct a comprehensive causality mining framework. This active transfer learning based framework along with its supplementary services, is able to extract and enrich, causal relationships and their corresponding entities from clinical text. ResultsThe multi-model transfer learning technique when applied over multiple iterations, gains substantial performance improvements. We also present a comparative analysis of the presented techniques with their common alternatives, which demonstrate the correctness of our approach and its ability to capture most causal relationships. ConclusionThe presented framework has provided cutting-edge results in the healthcare domain. However, the framework can be tweaked to provide causality detection in other domains, as well. SignificanceThe presented framework is generic enough to be utilized in any domain, healthcare services can gain massive benefits due to the voluminous and various nature of its data. This causal knowledge extraction framework can be used to summarize clinical text, create personas, discover medical knowledge, and provide evidence to clinical decision making.

Identifying False Human Papillomavirus (HPV) Vaccine Information and Corresponding Risk Perceptions From Twitter: Advanced Predictive Models.

BackgroundThe vaccination uptake rates of the human papillomavirus (HPV) vaccine remain low despite the fact that the effectiveness of HPV vaccines has been established for more than a decade. Vaccine hesitancy is in part due to false information about HPV vaccines on social media. Combating false HPV vaccine information is a reasonable step to addressing vaccine hesitancy.ObjectiveGiven the substantial harm of false HPV vaccine information, there is an urgent need to identify false social media messages before it goes viral. The goal of the study is to develop a systematic and generalizable approach to identifying false HPV vaccine information on social media.MethodsThis study used machine learning and natural language processing to develop a series of classification models and causality mining methods to identify and examine true and false HPV vaccine–related information on Twitter.ResultsWe found that the convolutional neural network model outperformed all other models in identifying tweets containing false HPV vaccine–related information (F score=91.95). We also developed completely unsupervised causality mining models to identify HPV vaccine candidate effects for capturing risk perceptions of HPV vaccines. Furthermore, we found that false information contained mostly loss-framed messages focusing on the potential risk of vaccines covering a variety of topics using more diverse vocabulary, while true information contained both gain- and loss-framed messages focusing on the effectiveness of vaccines covering fewer topics using relatively limited vocabulary.ConclusionsOur research demonstrated the feasibility and effectiveness of using predictive models to identify false HPV vaccine information and its risk perceptions on social media.

A Method for Mining Granger Causality Relationship on Atmospheric Visibility

Atmospheric visibility is an indicator of atmospheric transparency and its range directly reflects the quality of the atmospheric environment. With the acceleration of industrialization and urbanization, the natural environment has suffered some damages. In recent decades, the level of atmospheric visibility shows an overall downward trend. A decrease in atmospheric visibility will lead to a higher frequency of haze, which will seriously affect people's normal life, and also have a significant negative economic impact. The causal relationship mining of atmospheric visibility can reveal the potential relation between visibility and other influencing factors, which is very important in environmental management, air pollution control and haze control. However, causality mining based on statistical methods and traditional machine learning techniques usually achieve qualitative results that are hard to measure the degree of causality accurately. This article proposed the seq2seq-LSTM Granger causality analysis method for mining the causality relationship between atmospheric visibility and its influencing factors. In the experimental part, by comparing with methods such as linear regression, random forest, gradient boosting decision tree, light gradient boosting machine, and extreme gradient boosting, it turns out that the visibility prediction accuracy based on the seq2seq-LSTM model is about 10% higher than traditional machine learning methods. Therefore, the causal relationship mining based on this method can deeply reveal the implicit relationship between them and provide theoretical support for air pollution control.

WITHDRAWN: Predictive and causal chain mining to discover actionable knowledge from stock markets

Abstract In many scientific studies, discovering causal relationship is given high importance. Due to the availability of data in various domains, the research on mining causal relationships is growing steadily. Finding causal relationships from stock market datasets has its utility in that domain. Many researchers contributed towards developing algorithms for causal mining. In our prior work, we proposed algorithms for upstream and downstream causal relationships in stock market data. However, causal chain mining is relatively new research phenomenon where a set of inter-related actions are found and among the actions there exists causal relationship. Causal chains, in fact, reflect frequent occurrences of set of events with cause-effect relationship. In this paper we proposed a framework for mining causal relationships from stock market data. Two algorithms namely Stock Risk Prediction (SRP) and Causal Chain Miner (CCM) are proposed and implemented. An empirical study is made with a prototype application to validate the proposed framework. The experimental results showed the significance and usage of proposed causal chain mining and predictive algorithms which has comparable performance over the state of the art methods.

A Causality Mining and Knowledge Graph Based Method of Root Cause Diagnosis for Performance Anomaly in Cloud Applications

With the development of cloud computing technology, the microservice architecture (MSA) has become a prevailing application architecture in cloud-native applications. Many user-oriented services are supported by many microservices, and the dependencies between services are more complicated than those of a traditional monolithic architecture application. In such a situation, if an anomalous change happens in the performance metric of a microservice, it will cause other related services to be downgraded or even to fail, which would probably cause large losses to dependent businesses. Therefore, in the operation and maintenance job of cloud applications, it is critical to mine the causality of the problem and find its root cause as soon as possible. In this paper, we propose an approach for mining causality and diagnosing the root cause that uses knowledge graph technology and a causal search algorithm. We verified the proposed method on a classic cloud-native application and found that the method is effective. After applying our method on most of the services of a cloud-native application, both precision and recall were over 80%.

Haze pollution causality mining and prediction based on multi-dimensional time series with PS-FCM

Haze has become a frequent disastrous weather condition in China. Its formation consists of evolution process of several pollutants in certain meteorological conditions with complex relationships among these factors of haze formation. How to explore the complex relationships among these multi-dimensional factors as well as effectively predict them have become a key issue in research community. The research in this paper presents a method to quantitatively reveal causality in the formation of haze, which can be used to effectively predict haze pollution. In order to make the complicated relationship among different factors be interpretable, an PS-FCM (Primary Sub-Fuzzy Cognitive Maps) model is proposed and its multi-dimensional causality solution is demonstrated. By considering the formation of haze as an evolving process with time, we explore and discover the causality based on time series data of haze pollution with PS-FCM. Thus, a multi-dimensional time series data mining method based on the PS-FCM is developed to investigate the formation of haze. We validate our model by comparing with other machine learning method via experimental data and discuss the performance of PS-FCM under different transformation functions. The results explicitly show the quantitative causality among the different factors in haze formation.

Combining object detection and causality mining for efficient development of augmented reality-based on-the-job training systems in hotel management

ABSTRACTThe purpose of this study is to propose a methodology for establishing an augmented reality (AR)-based model for efficient OJT through object detection and causality mining, a novel text analysis method. Articles on hotel management published in the last decade and useful for OJT were collected, information on the causal relationships between them extracted, and related rules saved to a rule base. Using the same data, we detected various objects through SSD (Single Shot Multibox Detector), a real-time object detection system. Then we matched sets of causalities and displayed them to trainees wearing AR devices. This methodology reduces development and maintenance costs required to operate OJT programmes. Trainees are immersed in the training environment, which improves the effectiveness of the training. To show the feasibility of the proposed method, we developed a prototype AR-OJT system for hotel management training, automatically extracting knowledge on hotel management from articles according to the proposed method. The results demonstrate that the AR-OJT group shows better performance in terms of learning motivation and self-regulated learning than the control OJT group. No significant difference in learning performance was found between the two groups, which implies that traditional OJT can be substituted with AR-OJT.

A systematic approach for discovering causal dependencies between observations and incidents in the health and safety domain

The paper at hand motivates, proposes, demonstrates, and evaluates a novel systematic approach to discovering causal dependencies between events encoded in large arrays of data, called causality mining. The approach has emerged in the discussions with our project partner, an Australian public energy company. It was successfully evaluated in a case study with the project partner to extract valuable, and otherwise unknown, information on the causal dependencies between observations reported by the company’s employees as part of the organizational health and safety management practices and incidents that had occurred at the organization’s sites. The dependencies were derived based on the notion of proximity of the observations and incidents. The setup and results of the evaluation are reported in this paper. The new approach and the delivered insights aim at improving the overall health and safety culture of the project partner practices, as they can be applied to caution and, thus, prevent future incidents.

Applying Dynamic Causal Mining in Health Service Management

This article describes an application that illustrates the role of data mining technology in identifying hidden causal knoledge from health and medical data repositories. Across the health care and medical enterprises, a wide variety of data is being generated at a rapid rate. Current information technologies tends to focus on a more statical side of causal knowledge and do not address the dynamic causal knowledge. This article shows that the dynamic causal relation data can be captured for treatment, payment, operations purposes and administrative directed insights. Accessing this currently unrealized knowledge potential would enable the delivery of actionable knowledge to medical practitioners, healthcare system managers, policy planners and even patients to make a significant difference in overall healthcare.