Causality Detection Research Articles

The association between the propensity to experience meaningful coincidence and brain anatomy in healthy females: The moderating role of coping skills

When two events co-occur within a specific time interval, some people experience ‘meaningful coincidence’. This may be a consequence of the mind searching for causal structure in reality. In cases of negative events, it may be a coping strategy for managing stress. The present voxel-based morphometry (VBM) study investigated neural correlates of the propensity to experience meaningful coincidence (PEMC). VBM data from 115 females (mean age: 26 years) were correlated with self-reported PEMC and the use of certain coping strategies (e.g. seeking support, positive focusing). PEMC was negatively correlated with grey matter volume (GMV) in the medial prefrontal cortex, the inferior frontal gyrus, and the superior/inferior parietal cortex. Moderation analyses indicated that the negative association between GMV in the mentioned brain regions and PEMC was only present in participants with average or below-average coping skills. The identified fronto-parietal regions are part of an integrated neural network implicated in the detection of causality and cognitive control.

Diverse Causality Inference in Foreign Exchange Markets

The relationship between currencies in foreign exchange markets has been a topic of significance in economics. Previous studies have focused more on correlations between currencies. However, the detection of causality can reveal their inherent laws. Although the traditional Granger causality test can identify causality, it cannot take into account the nature and intensity of the causality. Thus, the objective of this paper is to identify the causalities of currencies from the perspective of dynamics. In this paper, we select 25 currencies (with the US dollar (USD) as the numeraire) from foreign exchange markets, as they occupy large shares in their regions. To detect the causalities of the foreign exchange markets, we combine PC (pattern causality) theory and complex networks to construct directed and weighted causality networks, in which the nodes represent the currencies and the directed edges represent the causal intensities. Furthermore, we study the symmetry of each causality and quantify the symmetry degree. The results demonstrate that causalities exist between currencies that differ in terms of nature and intensity. The positive causality network exhibits substantial robustness, which can be regarded as the dominant causal relationship in the foreign exchange markets, although a few exceptions are encountered, such as the dominant negative and disordered causalities between currency pairs. In addition, the dominant causalities between most currencies are symmetric in terms of nature, and they also exhibit symmetry in terms of intensity. Furthermore, by gradually deleting the network by thresholding according to the edge weights, we identify the important driving currencies of the markets. This paper may provide valuable information for investors and supervisory departments.

Granger Causality on forward and Reversed Time Series.

In this study, the information flow time arrow is investigated for stochastic data defined by vector autoregressive models. The time series are analyzed forward and backward by different Granger causality detection methods. Besides the normal distribution, which is usually required for the validity of Granger causality analysis, several other distributions of predictive errors are considered. A clear effect of a change in the order of cause and effect on the time-reversed series of unidirectionally connected variables was detected with standard Granger causality test (GC), when the product of the connection strength and the ratio of the predictive errors of the driver and the recipient was below a certain level, otherwise bidirectional causal connection was detected. On the other hand, opposite causal link was detected unconditionally by the methods based on the time reversal testing, but they were not able to detect correct bidirectional connection. The usefulness of the backward analysis is manifested in cases where falsely detected unidirectional connections can be rejected by applying the result obtained after the time reversal, and in cases of uncorrelated causally independent variables, where the absence of a causal link detected by GC on the original series should be confirmed on the time-reversed series.

Causal network reconstruction from nonlinear time series: A comparative study

Reconstructing causal networks from observed time series is a common problem in diverse science and engineering fields, and the challenge can increase with network size. Unfortunately, systematic comparisons of causal inference techniques have tended to concentrate on small motifs rather than graphs which it would be natural to label as complex networks. In this paper, six widely used methods of causal network reconstruction are systematically benchmarked and contrasted using neuronal models coupled on networks of varied size. Our purpose is concisely to review and explain the basic problems of causality detection using time series, and to compare the performance of varied and practical methods under the extremely relevant, but relatively neglected, conditions of highly nonlinear dynamical systems coupled via networks of many nodes. We find that convergent cross mapping consistently provides the highest precision, but transfer entropy can be preferable when high recall is important. The advantages of convergent cross mapping and transfer entropy over other methods can increase with network size.

Neural Granger Causality.

While most classical approaches to Granger causality detection assume linear dynamics, many interactions in real-world applications, like neuroscience and genomics, are inherently nonlinear. In these cases, using linear models may lead to inconsistent estimation of Granger causal interactions. We propose a class of nonlinear methods by applying structured multilayer perceptrons (MLPs) or recurrent neural networks (RNNs) combined with sparsity-inducing penalties on the weights. By encouraging specific sets of weights to be zero-in particular, through the use of convex group-lasso penalties-we can extract the Granger causal structure. To further contrast with traditional approaches, our framework naturally enables us to efficiently capture long-range dependencies between series either via our RNNs or through an automatic lag selection in the MLP. We show that our neural Granger causality methods outperform state-of-the-art nonlinear Granger causality methods on the DREAM3 challenge data. This data consists of nonlinear gene expression and regulation time courses with only a limited number of time points. The successes we show in this challenging dataset provide a powerful example of how deep learning can be useful in cases that go beyond prediction on large datasets. We likewise illustrate our methods in detecting nonlinear interactions in a human motion capture dataset.

Causality analysis in process control based on denoising and periodicity-removing CCM

Purpose1. To improve the causality analysis performance, a novel causality detector based on time-delayed convergent cross mapping (TD-CCM) is proposed in this work. 2. Identify the root cause of plant-wide oscillations in process control system.Design/methodology/approachA novel causality analysis framework is proposed based on denoising and periodicity-removing TD-CCM (time-delayed convergent cross mapping). We first point out that noise and periodicity have adverse effects on causality detection. Then, the empirical mode decomposition (EMD) and detrended fluctuation analysis (FDA) are combined to achieve denoising. The periodicities are effectively removed through singular spectrum analysis (SSA). Following, the TD-CCM can accurately capture the causalities and locate the root cause by analyzing the filtered signals.Findings1. A novel causality detector based on denoising and periodicity-removing time-delayed convergent cross mapping (TD-CCM) is proposed. 2. Simulation studies show that the proposed method is able to improve the causality analysis performance. 3. Industrial case study shows the proposed method can be used to analyze the root cause of plant-wide oscillations in process control system.Originality/value1. A novel causality detector based on denoising and periodicity-removing time-delayed convergent cross mapping (TD-CCM) is proposed. 2. The influences of noise and periodicity on causality analysis are investigated. 3. Simulations and industrial case shows that the proposed method can improve the causality analysis performance and can be used to identify the root cause of plant-wide oscillations in process control system.

Інструментарій причинно-наслідкового висновку: огляд та перспективи

Introduction. The need to establish causality covers a fairly wide range of different industries with different specifics and approaches. Therefore, it becomes necessary to apply various methods to solve the assigned tasks (in the context of causality), which is accompanied by the choice of a wide range of tools, depending on the task at hand. Purpose. The purpose of this work is a brief overview and analysis of modern methods, algorithms and technologies for detecting causation and the range of tasks in which the use of the appropriate tools takes place. Methods. Starting from the gold standards of causal identification and to more accurate, but limited by the range of conditions, algorithms, the current state, advantages and disadvantages of the use of tools are described. Result. The analysis of the current state of existing methods, algorithms and technologies for establishing causality is carried out, the prospects for further development and improvement of tools for causal detection are examined. Conclusions. At the moment there is a large list of known methods, algorithms and technologies, there is a number of problems in which there is a need for more accurate detection of causality. The paper shows that most of the tools for establishing causality give good results for acyclic structures, at the same time, they can give false positive conclusions for cyclic structures. Well-known world scientific institutions and leading corporations of computer technology are fruitfully engaged in the development and implementation of more and more perfect tools for establishing causality in order to develop automated software projects close to human thinking.

Granger causality detection in high-dimensional systems using feedforward neural networks

This paper proposes a novel methodology to detect Granger causality on average in vector autoregressive settings using feedforward neural networks. The approach accommodates unknown dependence structures between elements of high-dimensional multivariate time series with weak and strong persistence. To do this, we propose a two-stage procedure: first, we maximize the transfer of information between input and output variables in the network in order to obtain an optimal number of nodes in the intermediate hidden layers. Second, we apply a novel sparse double group lasso penalty function in order to identify the variables that have the predictive ability and, hence, indicate that Granger causality is present in the others. The penalty function inducing sparsity is applied to the weights that characterize the nodes of the neural network. We show the correct identification of these weights so as to increase sample sizes. We apply this method to the recently created Tobalaba network of renewable energy companies and show the increase in connectivity between companies after the creation of the network using Granger causality measures to map the connections.

Information-theoretic measures for nonlinear causality detection: application to social media sentiment and cryptocurrency prices

Information transfer between time series is calculated using the asymmetric information-theoretic measure known as transfer entropy. Geweke’s autoregressive formulation of Granger causality is used to compute linear transfer entropy, and Schreiber’s general, non-parametric, information-theoretic formulation is used to quantify nonlinear transfer entropy. We first validate these measures against synthetic data. Then we apply these measures to detect statistical causality between social sentiment changes and cryptocurrency returns. We validate results by performing permutation tests by shuffling the time series, and calculate the Z-score. We also investigate different approaches for partitioning in non-parametric density estimation which can improve the significance. Using these techniques on sentiment and price data over a 48-month period to August 2018, for four major cryptocurrencies, namely bitcoin (BTC), ripple (XRP), litecoin (LTC) and ethereum (ETH), we detect significant information transfer, on hourly timescales, with greater net information transfer from sentiment to price for XRP and LTC, and instead from price to sentiment for BTC and ETH. We report the scale of nonlinear statistical causality to be an order of magnitude larger than the linear case.

Implementation of two causal methods based on predictions in reconstructed state spaces.

If deterministic dynamics is dominant in the data, then methods based on predictions in reconstructed state spaces can serve to detect causal relationships between and within the systems. Here we introduce two algorithms for such causal analysis. They are designed to detect causality from two time series but are potentially also applicable in a multivariate context. The first method is based on cross-predictions, and the second one on the so-called mixed predictions. In terms of performance, the cross-prediction method is considerably faster and less prone to false negatives. The predictability improvement method is slower, but in addition to causal detection, in a multivariate scenario, it also reveals which specific observables can help the most if we want to improve prediction. The study also highlights cases where our methods and state-space approaches generally seem to lose reliability. We propose a new perspective on these situations, namely that the variables under investigation have weak observability due to the complex nonlinear information flow in the system. Thus, in such cases, the failure of causality detection cannot be attributed to the methods themselves but to the use of data that do not allow reliable reconstruction of the underlying dynamics.

A deep learning model for mining and detecting causally related events in tweets

AbstractNowadays, public gatherings and social events are an integral part of a modern city life. To run such events seamlessly, it requires real time mining and monitoring of causally related events so that the management can make informed decisions and take appropriate actions. The automatic detection of event causality from short text such as tweets could be useful for event management in this context. However, detecting event causality from tweets is a challenging task. Tweets are short, unstructured, and often written in highly informal language which lacks enough contextual information to detect causality. The existing approaches apply different techniques including hand‐crafted linguistic rules and machine learning models. However, none of the approaches tackle the issue related to the lack of contextual information. In this paper, we detect event causality in tweets by applying a context word extension technique and a deep causal event detection model. The context word extension technique is driven by background knowledge extracted from one million news articles. Our model achieves 79.35% recall and 67.28% f1‐score, which are 17.39% and 2.33% improvements to the state‐of‐the‐art approach.

Image-Based Methods to Investigate Synchronization between Time Series Relevant for Plasma Fusion Diagnostics.

Advanced time series analysis and causality detection techniques have been successfully applied to the assessment of synchronization experiments in tokamaks, such as Edge Localized Modes (ELMs) and sawtooth pacing. Lag synchronization is a typical strategy for fusion plasma instability control by pace-making techniques. The major difficulty, in evaluating the efficiency of the pacing methods, is the coexistence of the causal effects with the periodic or quasi-periodic nature of the plasma instabilities. In the present work, a set of methods based on the image representation of time series, are investigated as tools for evaluating the efficiency of the pace-making techniques. The main options rely on the Gramian Angular Field (GAF), the Markov Transition Field (MTF), previously used for time series classification, and the Chaos Game Representation (CGR), employed for the visualization of large collections of long time series. The paper proposes an original variation of the Markov Transition Matrix, defined for a couple of time series. Additionally, a recently proposed method, based on the mapping of time series as cross-visibility networks and their representation as images, is included in this study. The performances of the method are evaluated on synthetic data and applied to JET measurements.

Non-Uniform Embedding Scheme and Low-Dimensional Approximation Methods for Causality Detection.

Information causality measures have proven to be very effective in uncovering the connectivity patterns of multivariate systems. The non-uniform embedding (NUE) scheme has been developed to address the “curse of dimensionality”, since the estimation relies on high-dimensional conditional mutual information (CMI) terms. Although the NUE scheme is a dimension reduction technique, the estimation of high-dimensional CMIs is still required. A possible solution is the utilization of low-dimensional approximation (LA) methods for the computation of CMIs. In this study, we aim to provide useful insights regarding the effectiveness of causality measures that rely on NUE and/or on LA methods. In a comparative study, three causality detection methods are evaluated, namely partial transfer entropy (PTE) defined using uniform embedding, PTE using the NUE scheme (PTENUE), and PTE utilizing both NUE and an LA method (LATE). Results from simulations on well known coupled systems suggest the superiority of PTENUE over the other two measures in identifying the true causal effects, having also the least computational cost. The effectiveness of PTENUE is also demonstrated in a real application, where insights are presented regarding the leading forces in financial data.

Association of bone mineral density with nine urinary personal care and consumer product chemicals and metabolites: A national-representative, population-based study

Personal care and consumer products are extensively used and contain various chemicals that are recognized as endocrine disrupting chemicals. Few epidemiological studies measured the associations of these chemicals and metabolites, especially phenols, with bone health. We measured the associations between nine urinary phenol biomarkers and BMD and the subsequent prevalence of osteopenia/osteoporosis. National representative data from US adults aged over 20 years who participated in the National Health and Nutrition Examination Survey from 2005 to 2010 were used. 2,267 men, 1,145 premenopausal women and 1,033 postmenopausal women were included in the final analyses. Bisphenol A, benzophenone-3, triclosan, butylparaben, ethylparaben, methylparaben, propylparaben, 2,5-dichlorophenol and 2,4-dichlorophenol were measured by high-performance liquid chromatography-isotope dilution tandem mass spectrometry. Paraben concentrations, especially ethyl-, methyl- and propylparabens, were related to greater BMD in the femoral neck and intertrochanter and lumbar spine in men and premenopausal women. 2,4-dichlorophenol concentrations was associated with lower BMD and a higher prevalence of osteopenia + osteoporosis in men. Bisphenol A was associated with a higher prevalence of osteopenia + osteoporosis in the lumbar spine in postmenopausal women. Benzophenone-3 tended to be associated with a higher BMD of femur in men and premenopausal women. In conclusion, certain urinary personal care and consumer product chemicals and metabolites were significantly associated with BMD or osteopenia + osteoporosis. These results further indicate that, in future research, determination of the causal relationships and detection of undelying mechanism could be next important directions.

Partial cross mapping eliminates indirect causal influences

Causality detection likely misidentifies indirect causations as direct ones, due to the effect of causation transitivity. Although several methods in traditional frameworks have been proposed to avoid such misinterpretations, there still is a lack of feasible methods for identifying direct causations from indirect ones in the challenging situation where the variables of the underlying dynamical system are non-separable and weakly or moderately interacting. Here, we solve this problem by developing a data-based, model-independent method of partial cross mapping based on an articulated integration of three tools from nonlinear dynamics and statistics: phase-space reconstruction, mutual cross mapping, and partial correlation. We demonstrate our method by using data from different representative models and real-world systems. As direct causations are keys to the fundamental underpinnings of a variety of complex dynamics, we anticipate our method to be indispensable in unlocking and deciphering the inner mechanisms of real systems in diverse disciplines from data.

Statistical Causality for Multivariate Non-Linear Time Series via Gaussian Processes

The ability to test for statistical causality in linear and non-linear contexts, in stationary or non-stationary settings and to identify whether statistical causality influences trend of volatility forms a piratically important class of problems to explore in multi-modal and multivariate processes. In this paper we develop novel testing frameworks for statistical causality in general classes of multi-variate non-linear time-series models. Our framework accommodates flexible features where causality may be present in either: trend, volatility or both structural components of the general multivariate Markov processes under study. In addition, we accommodate the added possibilities of flexible structural features such as long memory and persistence in the multivariate processes when applying our semi-parametric approach to causality detection. We design a calibration procedure and formal testing procedure to detect these relationships through classes of Gaussian process models. We provide a generic framework which can be applied to a wide range of problems, including partially observed generalised diffusions or general multivariate linear or non-linear time series models. We develop several illustrative examples of features that are easily testable under our framework to study the properties of the inference procedure developed including power of the test, sensitivity and robustness. We then illustrate our method on an interesting real data example from commodity modelling.

The Data Mining Group at University of Vienna

How can we extract meaningful knowledge from massive amounts of data? The data mining group at University of Vienna contributes novel methods for exploratory data analysis. Our main research focus is on unsupervised learning, where we want to identify any kind of non-random structure or patterns in the data without restricting ourselves to a pre-defined target variable or analysis goal. Our major lines of current research are clustering, causality detection and highly efficient exploratory data analysis on massive data. Besides that, we develop application-specific methods addressing specific challenges in biomedicine, neuroscience and environmental sciences. In teaching, we offer fundamental and advanced courses in data mining, machine learning and scientific data management for Bachelor and Master students of computer science and related programs.

Debates—Does Information Theory Provide a New Paradigm for Earth Science? Causality, Interaction, and Feedback

AbstractThe concept of causal interactions between components is an integral part of hydrology and Earth system sciences. Modelers, decision makers, scientists, and other water resources stakeholders all utilize some notion of cause‐and‐effect to understand processes, make decisions, and infer how systems react to change. However, there are different perspectives on the meaning of causality in complex systems and, further, different frameworks and methodologies with which to detect causal interactions. We propose here that information theory (IT) provides a compelling framework for the detection of causality and discuss approaches for several levels of analyses that capture interactions that range from pairwise to multivariate in nature. We illustrate these types of analyses with an example based on weather station time series variables, in which variables may interact pairwise or jointly and on short to long time scales. In general, many unsolved or even unanticipated questions in the hydrologic sciences could benefit from this perspective.

Fusarium wilt: a threat to banana cultivation and its management.

Abstract Banana is affected by a wide number of diseases, of which, Fusarium wilt caused by Fusarium oxysporum f. sp. cubense ( Foc ) race 1 has played a major role in devastating Gros Michel banana plantations. Since 1960s, the pathogen Foc race 4 has threatened the survival and existence of the Cavendish group of bananas, which has necessitated detailed study on Fusarium wilt, the causal organism Foc , its biology, dispersal, pathogenicity, diversity and detection at a molecular level (especially in soils) and its management. The recently developed technique of transferring the gene encoding green fluorescent protein into Foc has assisted in visualizing and analysing the colonization and infection of banana plants by the pathogen. Studies on the pathogenicity secreted in xylem genes have helped in rapid detection of the pathogen in planta and techniques such as real-time fluorescence loop-mediated isothermal amplification assay have facilitated rapid and direct quantitative detection of Foc in soil. Several management practices, especially resistant varieties/transgenics and biological control methods are available for the effective management of this deadly disease. Strict quarantine procedures and reduction of Foc inoculum are the methods undertaken to limit the spread of the disease to other un-infected regions. This review summarizes the recent developments of Fusarium wilt in banana and its management.

Granger Causality Detection in High-Dimensional Systems Using Feedforward Neural Networks

This paper proposes a novel methodology to detect Granger causality in mean in vector autoregressive settings using feedforward neural networks. The approach accommodates unknown dependence structures between the elements of highly-dimensional multivariate time series with weak and strong persistence. To do this, we propose a two-stage procedure. First, we fit a neural network given by an optimal number of nodes in the intermediate hidden layers. This is done by maximising the transfer of information between input and output variables in the network. Second, we apply a novel sparse double group lasso penalty function to identify the variables that have predictive ability and, hence, Granger cause the others. The penalty function inducing sparsity is applied to the weights characterizing the nodes of the neural network. We show the correct identification of these weights for increasing sample sizes. A comprehensive simulation study shows the strong performance of our method for Granger causality detection in terms of size and power, and the consistency of the method for model selection for increasing sample sizes. An application to the recently created Tobalaba network of renewable energy companies shows the increase in connectivity between companies after the creation of the network using Granger-causality measures to map the connections.