Causality Detection Research Articles

Supervised Estimation of Granger-Based Causality between Time Series.

Brain effective connectivity aims to detect causal interactions between distinct brain units and it is typically studied through the analysis of direct measurements of the neural activity, e.g., magneto/electroencephalography (M/EEG) signals. The literature on methods for causal inference is vast. It includes model-based methods in which a generative model of the data is assumed and model-free methods that directly infer causality from the probability distribution of the underlying stochastic process. Here, we firstly focus on the model-based methods developed from the Granger criterion of causality, which assumes the autoregressive model of the data. Secondly, we introduce a new perspective, that looks at the problem in a way that is typical of the machine learning literature. Then, we formulate the problem of causality detection as a supervised learning task, by proposing a classification-based approach. A classifier is trained to identify causal interactions between time series for the chosen model and by means of a proposed feature space. In this paper, we are interested in comparing this classification-based approach with the standard Geweke measure of causality in the time domain, through simulation study. Thus, we customized our approach to the case of a MAR model and designed a feature space which contains causality measures based on the idea of precedence and predictability in time. Two variations of the supervised method are proposed and compared to a standard Granger causal analysis method. The results of the simulations show that the supervised method outperforms the standard approach, in particular it is more robust to noise. As evidence of the efficacy of the proposed method, we report the details of our submission to the causality detection competition of Biomag2014, where the proposed method reached the 2nd place. Moreover, as empirical application, we applied the supervised approach on a dataset of neural recordings of rats obtaining an important reduction in the false positive rate.

Non-linear Heart Rate and Blood Pressure Interaction in Response to Lower-Body Negative Pressure.

Early detection of hemorrhage remains an open problem. In this regard, blood pressure has been an ineffective measure of blood loss due to numerous compensatory mechanisms sustaining arterial blood pressure homeostasis. Here, we investigate the feasibility of causality detection in the heart rate and blood pressure interaction, a closed-loop control system, for early detection of hemorrhage. The hemorrhage was simulated via graded lower-body negative pressure (LBNP) from 0 to −40 mmHg. The research hypothesis was that a significant elevation of causal control in the direction of blood pressure to heart rate (i.e., baroreflex response) is an early indicator of central hypovolemia. Five minutes of continuous blood pressure and electrocardiogram (ECG) signals were acquired simultaneously from young, healthy participants (27 ± 1 years, N = 27) during each LBNP stage, from which heart rate (represented by RR interval), systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean arterial pressure (MAP) were derived. The heart rate and blood pressure causal interaction (RR↔SBP and RR↔MAP) was studied during the last 3 min of each LBNP stage. At supine rest, the non-baroreflex arm (RR→SBP and RR→MAP) showed a significantly (p < 0.001) higher causal drive toward blood pressure regulation compared to the baroreflex arm (SBP→RR and MAP→RR). In response to moderate category hemorrhage (−30 mmHg LBNP), no change was observed in the traditional marker of blood loss i.e., pulse pressure (p = 0.10) along with the RR→SBP (p = 0.76), RR→MAP (p = 0.60), and SBP→RR (p = 0.07) causality compared to the resting stage. Contrarily, a significant elevation in the MAP→RR (p = 0.004) causality was observed. In accordance with our hypothesis, the outcomes of the research underscored the potential of compensatory baroreflex arm (MAP→RR) of the heart rate and blood pressure interaction toward differentiating a simulated moderate category hemorrhage from the resting stage. Therefore, monitoring baroreflex causality can have a clinical utility in making triage decisions to impede hemorrhage progression.

Driver workload estimation using a novel hybrid method of error reduction ratio causality and support vector machine

Measuring driver workload is of great significance for improving the understanding of driver behaviours and supporting the improvement of advanced driver assistance systems technologies. In this paper, a novel hybrid method for measuring driver workload estimation for real-world driving data is proposed. Error reduction ratio causality, a new nonlinear causality detection approach, is being proposed in order to assess the correlation of each measured variable to the variation of workload. A full model describing the relationship between the workload and the selected important measurements is then trained via a support vector regression model. Real driving data of 10 participants, comprising 15 measured physiological and vehicle-state variables are used for the purpose of validation. Test results show that the developed error reduction ratio causality method can effectively identify the important variables that relate to the variation of driver workload, and the support vector regression based model can successfully and robustly estimate workload.

P 29 Brain mechanisms for phenomenal causation and oddball-perceptions – combining fMRI and high-frequent eye-tracking – A potential fundament for the first perceptual treatment of psychotic symptoms?

Brain mechanisms for phenomenal causation and oddball-perceptions – combining fMRI and high-frequent eye-tracking: a potential fundament for the first perceptual treatment of psychotic symptoms? Causality is a fundamental aspect of human perception, enabling us to make sense of events occurring in the world. Causality is also an often underestimated, although critical, feature of agency attribution, a fundamental cognitive function that is well-known to be perturbed in psychosis. The neuronal and psychophysiological correlates of causal perception are thus of particular interest for clinical neuroscientific research focused on psychotic symptomatology: Especially for research aimed at the development of potential cognitive or perceptual interventions to the core symptomatology of schizophrenia and malfunctionous agency attributions. The most important paradigm to investigate perceptual causality has been designed more than 70 years ago by Gestalt-Psychologist Albert Michotte (Michotte, 1946/1963). He has first shown that collisions of abstract moving stimuli (so-called “launch-events”), for example a collision of two billiard balls, evoke in healthy humans “Gestalt-perceptions of physical causality”, given that the motion event as a whole is contingent with the laws of mechanics/transfer of momentum (see for review e.g. Wagemans et al., 2006). Previous behavioral research indicates, that patients with psychosis show altered attributions of causality to launch-events (Tschacher and Kupper, 2006). Neuroimaging findings are further suggesting that the dysfunctional causality attributions occur in both physical and social event contexts, and are associated at the neural level with altered functional activity in the frontal cortex, as well as a disconnection of frontal and posterior cortex regions (Blakemore et al., 2003; Wende et al., 2014). However, an important confound of these studies is their reliance on explicit judgments or ratings of causality, making it impossible to infer from the results, if a higher-order cognitive (“top down”) or sensory-perceptual (“bottom up”) dysfunction is underlying the behavioral alterations. Here, eye-tracking can help to investigate the implicit component: Our paradigm is based on eye-movement monitoring at a real-time recording frequency (1 kHz, EyeLink 1000) during the observation of simultaneous launching displays (one launcher collides with two targets; one moves in a causal but non-colinear, the other one in a non-causal but colinear direction; Wende et al., 2015). Ocular (saccadic and pursuit) reactions to causal and non-causal target motions are thus for the first time compared within-trial, enabling us in a behavioral study, to confirm the Gestalt-perception hypothesis of causation (Michotte, 1946/1963), that the human visual system has encapsulated a rapidly operating and sub-attentional mechanism, which accomplishes an implicit detection of physical causality, as distinct from non-causality (“oddballs”): Both saccades (latencies) and smooth-pursuit (velocity) were biased towards the causal target motion (Wende et al., 2015). As a next step, we now aim to combine eye tracking and fMRI in order to investigate the functional neuroanatomy of this causality perception module in both healthy subjects and patients with psychosis. The long-term aim of our study set is the development of an interactive launching paradigm, to train causal inference at the sensory level in psychotic patients.

Causative impact of air pollution on evapotranspiration in the North China Plain

Atmospheric dispersion conditions strongly impact air pollution under identical surface emissions. The degree of air pollution in the Jing-Jin-Ji region is so severe that it may impose feedback on local climate. Reference evapotranspiration (ET0) plays a significant role in the estimation of crop water requirements, as well as in studies on climate variation and change. Since the traditional correlation analysis cannot capture the causality, we apply the convergent cross mapping method (CCM) in this study to observationally investigate whether the air pollution impacts ET0. The results indicate that southwest regions of Jing-Jin-Ji always suffer higher PM2.5 concentration than north regions through the whole year, and correlation analysis suggests that PM2.5 concentration has a significant negative effect on ET0 in most cities. The causality detection with CCM quantitatively demonstrates the significantly causative influence of PM2.5 concentration on ET0, higher PM2.5 concentration decreasing ET0. However, CCM analysis suggests that PM2.5 concentration has a relatively weak causal influence on ET0 while the correlation analysis gives the near zero correlation coefficient in Zhangjiakou city, indicating that the causative influence of PM2.5 concentration on ET0 is better revealed with CCM method than the correlation analysis. Considering that ET0 is strongly associated with crop water requirement, the amount of water for agricultural irrigation could be reduced at high PM2.5 concentrations. These findings can be utilized to improve the efficiency of water resources utilization, and reduce the exploiting amount of groundwater in the Jing-Jin-Ji region, although PM2.5 is detrimental to human health.

Investigating the Impact of Perturbations in Chemical Processes on Data-Based Causality Analysis. Part 1: Defining Desired Performance of Causality Analysis Techniques

In large-scale chemical and mineral processes, processing units are connected to each other through material flow, energy flow, or information flow (manifested by control loops). Data-based causality analysis may be may be employed to infer the cause-effect connections between measured variables. In the last decade research into the application of causality analysis approach to chemical processes has proven these data-based techniques to be useful, especially for fault diagnosis. However, the impact of perturbations caused by different process conditions on the ability of these techniques to identify causality has not been thoroughly investigated. Improved understanding of the strengths and weaknesses of these techniques for causality detection under different conditions is essential for streamlining their automated application. In this two-part paper, the impact of perturbations generated by conditions typical to chemical processes is analysed. In this first part, causality analysis techniques are reviewed with the intent to define the desired characteristics and desired causality detection performance of the techniques. In the second part a simple process simulation used to introduce perturbations and analyse their impact on the causality detection ability of transfer entropy and Granger causality.

Design of fault monitoring framework for multi-energy systems using Signed Directed Graph

In this paper, a new causal graph-based fault detection and isolation framework is developed. The Signed Directed Graph (SDG), as a graphical model for capturing process topology and connectivity to show the causal relationships between process variables, has been widely used in modeling and fault diagnosis of multi-energies systems. An SDG is usually built based on mathematical equations of process or based on process knowledge as described by piping and instrumentation diagrams, this is a complex and experience-dependent task. This paper introduces a methodology to built the SDG directly from Bond Graph. For the fault detection and isolation purpose, the generic model is able to deduce systematically qualitative fault signatures matrix from the qualitative redundancy relations. Whenever an abnormality was not isolated, a principal component analysis and a back/forward propagation through paths on the SDG model are combined so as to ameliorate the fault isolability.To highlight the efficiency and the performance of the developed algorithm, the multi-energy process case study is provided.

Investigating the Impact of Perturbations in Chemical Processes on Data-Based Causality Analysis. Part 2: Testing Granger Causality and Transfer Entropy

In this two-part paper, the impact of perturbations generated by conditions typical to chemical processes is analysed. In the first part, causality analysis techniques intended for fault diagnosis are discussed in order to define the desired characteristics of the techniques. In this second part a simple process simulation was used to introduce oscillatory and step perturbations and perform a sensitivity analysis of their impact on the causality detection ability of transfer entropy and Granger causality. This procedure was repeated for three types of operation: (1) a base case open loop operation with sensor noise added, (2) open loop with process noise added and (3) closed loop operation with only sensor noise. Granger causality and transfer entropy both proved robust at detecting causality under different conditions, with some exceptions where the causal relationship was obscured: very high frequency oscillations resembled random noise; for low frequency oscillations the observation window used by the causality techniques was too small to capture the gradual dynamics; closed loop operation attenuated slow acting oscillations and step inputs; and addition of process noise decreased the apparent strength of the causality. Comparing the two techniques: Granger causality proved more reliable than transfer entropy for oscillatory perturbations; the effect of the controller obscuring the causal connection was less pronounced for transfer entropy; and transfer entropy appeared much more sensitive to the influence of additional process noise.

Genetic Dissection of Heritable Traits in Yeast Using Bulk Segregant Analysis.

Bulk segregant analysis (BSA) is commonly used to determine the genetic basis of complex traits in yeast. This technique involves phenotyping progeny from a cross and then selectively genotyping pooled subsets of offspring with extreme phenotypes. Analysis of these genotype data can identify loci that show skewed allele frequencies in a group of phenotypically extreme individuals and that are likely to contribute to a trait. BSA can be applied to diverse strain crosses, including ones involving nonreference isolates. Further, given the high throughput of next-generation sequencing, it is possible to conduct many BSA experiments in parallel. Here, we present a BSA protocol for the generation of recombinant cross progeny. We then describe general BSA strategies for conducting phenotyping, causal loci detection, and candidate gene identification in a statistically powerful manner.

Real-Time Assessment and Diagnosis of Process Operating Performance

Over time, the performance of processes may deviate from the initial design due to process variations and uncertainties, making it necessary to develop systematic methods for online optimality assessment based on routine operating process data. Some processes have multiple operating modes caused by the set point change of the critical process variables to achieve different product specifications. On the other hand, the operating region in each operating mode can alter, due to uncertainties. In this paper, we will establish an optimality assessment framework for processes that typically have multi-mode, multi-region operations, as well as transitions between different modes. The kernel density approach for mode detection is adopted and improved for operating mode detection. For online mode detection, the model-based clustering discriminant analysis (MclustDA) approach is incorporated with some a priori knowledge of the system. In addition, multi-modal behavior of steady-state modes is tackled utilizing the mixture probabilistic principal component regression (MPPCR) method, and dynamic principal component regression (DPCR) is used to investigate transitions between different modes. Moreover, a probabilistic causality detection method based on the sequential forward floating search (SFFS) method is introduced for diagnosing poor or non-optimum behavior. Finally, the proposed method is tested on the Tennessee Eastman (TE) benchmark simulation process in order to evaluate its performance.

Evaluation of the accuracy of imputed sequence variant genotypes and their utility for causal variant detection in cattle

BackgroundThe availability of dense genotypes and whole-genome sequence variants from various sources offers the opportunity to compile large datasets consisting of tens of thousands of individuals with genotypes at millions of polymorphic sites that may enhance the power of genomic analyses. The imputation of missing genotypes ensures that all individuals have genotypes for a shared set of variants.ResultsWe evaluated the accuracy of imputation from dense genotypes to whole-genome sequence variants in 249 Fleckvieh and 450 Holstein cattle using Minimac and FImpute. The sequence variants of a subset of the animals were reduced to the variants that were included on the Illumina BovineHD genotyping array and subsequently inferred in silico using either within- or multi-breed reference populations. The accuracy of imputation varied considerably across chromosomes and dropped at regions where the bovine genome contains segmental duplications. Depending on the imputation strategy, the correlation between imputed and true genotypes ranged from 0.898 to 0.952. The accuracy of imputation was higher with Minimac than FImpute particularly for variants with a low minor allele frequency. Using a multi-breed reference population increased the accuracy of imputation, particularly when FImpute was used to infer genotypes. When the sequence variants were imputed using Minimac, the true genotypes were more correlated to predicted allele dosages than best-guess genotypes. The computing costs to impute 23,256,743 sequence variants in 6958 animals were ten-fold higher with Minimac than FImpute. Association studies with imputed sequence variants revealed seven quantitative trait loci (QTL) for milk fat percentage. Two causal mutations in the DGAT1 and GHR genes were the most significantly associated variants at two QTL on chromosomes 14 and 20 when Minimac was used to infer genotypes.ConclusionsThe population-based imputation of millions of sequence variants in large cohorts is computationally feasible and provides accurate genotypes. However, the accuracy of imputation is low in regions where the genome contains large segmental duplications or the coverage with array-derived single nucleotide polymorphisms is poor. Using a reference population that includes individuals from many breeds increases the accuracy of imputation particularly at low-frequency variants. Considering allele dosages rather than best-guess genotypes as explanatory variables is advantageous to detect causal mutations in association studies with imputed sequence variants.

Do trend extraction approaches affect causality detection in climate change studies?

Abstract Various scientific studies have investigated the causal link between solar activity (SS) and the earth’s temperature (GT). Results from literature indicate that both the detected structural breaks and existing trend have significant effects on the causality detection outcomes. In this paper, we make a contribution to this literature by evaluating and comparing seven trend extraction methods covering various aspects of trend extraction studies to date. In addition, we extend previous work by using Convergent Cross Mapping (CCM) - an advanced non-parametric causality detection technique to provide evidence on the effect of existing trend in global temperature on the causality detection outcome. This paper illustrates the use of a method to find the most reliable trend extraction approach for data preprocessing, as well as provides detailed analyses of the causality detection of each component by this approach to achieve a better understanding of the causal link between SS and GT. Furthermore, the corresponding CCM results indicate increasing significance of causal effect from SS to GT since 1880 to recent years, which provide solid evidences that may contribute on explaining the escalating global tendency of warming up recent decades.

Causality analysis detects the regulatory role of maternal effect genes in the early Drosophila embryo

In developmental studies, inferring regulatory interactions of segmentation genetic network play a vital role in unveiling the mechanism of pattern formation. As such, there exists an opportune demand for theoretical developments and new mathematical models which can result in a more accurate illustration of this genetic network. Accordingly, this paper seeks to extract the meaningful regulatory role of the maternal effect genes using a variety of causality detection techniques and to explore whether these methods can suggest a new analytical view to the gene regulatory networks. We evaluate the use of three different powerful and widely-used models representing time and frequency domain Granger causality and convergent cross mapping technique with the results being thoroughly evaluated for statistical significance. Our findings show that the regulatory role of maternal effect genes is detectable in different time classes and thereby the method is applicable to infer the possible regulatory interactions present among the other genes of this network.

Testing for causality in reconstructed state spaces by an optimized mixed prediction method.

In this study, a method of causality detection was designed to reveal coupling between dynamical systems represented by time series. The method is based on the predictions in reconstructed state spaces. The results of the proposed method were compared with outcomes of two other methods, the Granger VAR test of causality and the convergent cross-mapping. We used two types of test data. The first test example is a unidirectional connection of chaotic systems of Rössler and Lorenz type. The second one, the fishery model, is an example of two correlated observables without a causal relationship. The results showed that the proposed method of optimized mixed prediction was able to reveal the presence and the direction of coupling and distinguish causality from mere correlation as well.

Two step transfer entropy – An estimator of delayed directional couplings between multivariate EEG time series

Quantifying delayed directional couplings between electroencephalographic (EEG) time series requires an efficient method of causal network inference. This is especially due to the limited knowledge about the underlying dynamics of the brain activity. Recent methods based on information theoretic measures such as Transfer Entropy (TE) made significant progress on this issue by providing a model-free framework for causality detection. However, TE estimation from observed data is not a trivial task, especially when the number of variables is large which is the case in a highly complex system like human brain. Here we propose a computationally efficient procedure for TE estimation based on using sets of the Most Informative Variables that effectively contribute to resolving the uncertainty of the destination. In the first step of this method, some conditioning sets are determined through a nonlinear state space reconstruction; then in the second step, optimal estimation of TE is done based on these sets. Validation of the proposed method using synthetic data and neurophysiological signals demonstrates computational efficiency in quantifying delayed directional couplings compared with the common TE analysis.

Causal Relationship Detection in Archival Collections of Product Reviews for Understanding Technology Evolution

Technology progress is one of the key reasons behind today's rapid changes in lifestyles. Knowing how products and objects evolve can not only help with understanding the evolutionary patterns in our society but can also provide clues on effective product design and can offer support for predicting the future. We propose a general framework for analyzing technology's impact on our lives through detecting cause--effect relationships, where causes represent changes in technology while effects are changes in social life, such as new activities or new ways of using products. We address the challenge of viewing technology evolution through the “social impact lens” by mining causal relationships from the long-term collections of product reviews. In particular, we first propose dividing vocabulary into two groups: terms describing product features (called physical terms ) and terms representing product usage (called conceptual terms ). We then search for two kinds of changes related to the appearance of terms: frequency-based and context-based changes. The former indicate periods when a word was significantly more frequently used, whereas the latter indicate periods of high change in the word's context. Based on the detected changes, we then search for causal term pairs such that the change in the physical term triggers the change in the conceptual term. We next extend our approach to finding causal relationships between word groups such as a group of words representing the same technology and causing a given conceptual change or group of words representing two different technologies that simultaneously “co-cause” a conceptual change. We conduct experiments on different product types using the Amazon Product Review Dataset, which spans 1995 to 2013, and we demonstrate that our approaches outperform state-of-the-art baselines.

Causal impact of magnetic fluctuations in slow and fast L–H transitions at TJ-II

This work focuses on the relationship between L–H (or L–I) transitions and MHD activity in the low magnetic shear TJ-II stellarator. It is shown that the presence of a low order rational surface in the plasma edge (gradient) region lowers the threshold density for H-mode access. MHD activity is systematically suppressed near the confinement transition. We apply a causality detection technique (based on the Transfer Entropy) to study the relation between magnetic oscillations and locally measured plasma rotation velocity (related to Zonal Flows). For this purpose, we study a large number of discharges in two magnetic configurations, corresponding to “fast” and “slow” transitions. With the “slow” transitions, the developing Zonal Flow prior to the transition is associated with the gradual reduction of magnetic oscillations. The transition itself is marked by a strong spike of “information transfer” from magnetic to velocity oscillations, suggesting that the magnetic drive may play a role in setting up the final sheared flow responsible for the H-mode transport barrier. Similar observations were made for the “fast” transitions. Thus, it is shown that magnetic oscillations associated with rational surfaces play an important and active role in confinement transitions, so that electromagnetic effects should be included in any complete transition model.

A Complex Systems Approach to Causal Discovery in Psychiatry.

Conventional research methodologies and data analytic approaches in psychiatric research are unable to reliably infer causal relations without experimental designs, or to make inferences about the functional properties of the complex systems in which psychiatric disorders are embedded. This article describes a series of studies to validate a novel hybrid computational approach–the Complex Systems-Causal Network (CS-CN) method–designed to integrate causal discovery within a complex systems framework for psychiatric research. The CS-CN method was first applied to an existing dataset on psychopathology in 163 children hospitalized with injuries (validation study). Next, it was applied to a much larger dataset of traumatized children (replication study). Finally, the CS-CN method was applied in a controlled experiment using a ‘gold standard’ dataset for causal discovery and compared with other methods for accurately detecting causal variables (resimulation controlled experiment). The CS-CN method successfully detected a causal network of 111 variables and 167 bivariate relations in the initial validation study. This causal network had well-defined adaptive properties and a set of variables was found that disproportionally contributed to these properties. Modeling the removal of these variables resulted in significant loss of adaptive properties. The CS-CN method was successfully applied in the replication study and performed better than traditional statistical methods, and similarly to state-of-the-art causal discovery algorithms in the causal detection experiment. The CS-CN method was validated, replicated, and yielded both novel and previously validated findings related to risk factors and potential treatments of psychiatric disorders. The novel approach yields both fine-grain (micro) and high-level (macro) insights and thus represents a promising approach for complex systems-oriented research in psychiatry.

Effectiveness of whole-exome sequencing and costs of the traditional diagnostic trajectory in children with intellectual disability.

This study investigated whole-exome sequencing (WES) yield in a subset of intellectually disabled patients referred to our clinical diagnostic center and calculated the total costs of these patients' diagnostic trajectory in order to evaluate early WES implementation. We compared 17 patients' trio-WES yield with the retrospective costs of diagnostic procedures by comprehensively examining patient records and collecting resource use information for each patient, beginning with patient admittance and concluding with WES initiation. We calculated cost savings using scenario analyses to evaluate the costs replaced by WES when used as a first diagnostic tool. WES resulted in diagnostically useful outcomes in 29.4% of patients. The entire traditional diagnostic trajectory average cost was $16,409 per patient, substantially higher than the $3,972 trio-WES cost. WES resulted in average cost savings of $3,547 for genetic and metabolic investigations in diagnosed patients and $1,727 for genetic investigations in undiagnosed patients. The increased causal variant detection yield by WES and the relatively high costs of the entire traditional diagnostic trajectory suggest that early implementation of WES is a relevant and cost-efficient option in patient diagnostics. This information is crucial for centers considering implementation of WES and serves as input for future value-based research into diagnostics.Genet Med 18 9, 949-956.

Isolation of plant-wide faults using causality detection methods

Isolation of plant-wide faults in large-scale complex systems is particularly challenging. A methodology to detect and isolate faults is proposed, detecting the faulty variables using univariate control charts and the causality information between them to indicate the source. The clustering of faulty variables uses univariate analysis to avoid the smearing effect brought by multivariate analysis. The variable where the fault took place is indicated, handling fault novelties in a very natural manner. The proposed method is discussed and illustrated through its application to the Tennessee Eastman Process and to routine operating data from a thermoelectric power plant.