A Partial Correlation-Based Algorithm for Causal Structure Discovery with Continuous Variables

Pellet, Jean-Philippe; Elisseeff, André

doi:10.1007/978-3-540-74825-0_21

Cited by 14 publications

(10 citation statements)

References 7 publications

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“…To do so, information about the causal relationship between the variables is required. This information can be either obtained by a priori knowledge of the process or through network reconstruction techniques, as for instance the ones found in the works of Fuente, Bing, Hoeschele and Mendes [35] and Pellet and Elisseeff [36]. The knowledge of the casual network can then be used to successively fit regression models for each variable by considering as regressors its causal parents.…”

Section: Sensitivity Enhancing Transformationmentioning

confidence: 99%

On-line process monitoring using local measures of association: Part I — Detection performance

Rato

Reis

2015

Chemometrics and Intelligent Laboratory Systems

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Section: Sensitivity Enhancing Transformationmentioning

confidence: 99%

On-line process monitoring using local measures of association: Part I — Detection performance

Rato

Reis

2015

Chemometrics and Intelligent Laboratory Systems

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“…In practice, it require sufficient samples to conduct reliable CI tests. However, reliable CI tests are commonly assumed in the theoretical correctness analysis of Bayesian network learning [Cheng et al 2002;Margaritis and Thrun 1999;Pellet and Elisseeff 2007;Tsamardinos et al 2006]. …”

Section: Estimating the Svars By Bayesian Network Learning Algorithmmentioning

confidence: 99%

Learning Causal Relations in Multivariate Time Series Data

Wang

Chan

2012

ACM Trans. Intell. Syst. Technol.

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Many applications naturally involve time series data and the vector autoregression (VAR), and the structural VAR (SVAR) are dominant tools to investigate relations between variables in time series. In the first part of this work, we show that the SVAR method is incapable of identifying contemporaneous causal relations for Gaussian process. In addition, least squares estimators become unreliable when the scales of the problems are large and observations are limited. In the remaining part, we propose an approach to apply Bayesian network learning algorithms to identify SVARs from time series data in order to capture both temporal and contemporaneous causal relations, and avoid high-order statistical tests. The difficulty of applying Bayesian network learning algorithms to time series is that the sizes of the networks corresponding to time series tend to be large, and high-order statistical tests are required by Bayesian network learning algorithms in this case. To overcome the difficulty, we show that the search space of conditioning sets d-separating two vertices should be a subset of the Markov blankets. Based on this fact, we propose an algorithm enabling us to learn Bayesian networks locally, and make the largest order of statistical tests independent of the scales of the problems. Empirical results show that our algorithm outperforms existing methods in terms of both efficiency and accuracy.

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“…Partial correlations also constitute a viable solution to extract the network of direct relationships linking the observed variables [111][112][113]. Therefore, they present the potential to be incorporated in effective data-driven structured IPM approaches.…”

Section: Data-driven Structured Approaches For Process Diagnosis: Netmentioning

confidence: 99%

Industrial Process Monitoring in the Big Data/Industry 4.0 Era: from Detection, to Diagnosis, to Prognosis

Reis

Gins²

2017

Processes

222

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We provide a critical outlook of the evolution of Industrial Process Monitoring (IPM) since its introduction almost 100 years ago. Several evolution trends that have been structuring IPM developments over this extended period of time are briefly referred, with more focus on data-driven approaches. We also argue that, besides such trends, the research focus has also evolved. The initial period was centred on optimizing IPM detection performance. More recently, root cause analysis and diagnosis gained importance and a variety of approaches were proposed to expand IPM with this new and important monitoring dimension. We believe that, in the future, the emphasis will be to bring yet another dimension to IPM: prognosis. Some perspectives are put forward in this regard, including the strong interplay of the Process and Maintenance departments, hitherto managed as separated silos.

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A Partial Correlation-Based Algorithm for Causal Structure Discovery with Continuous Variables

Cited by 14 publications

References 7 publications

On-line process monitoring using local measures of association: Part I — Detection performance

On-line process monitoring using local measures of association: Part I — Detection performance

Learning Causal Relations in Multivariate Time Series Data

Industrial Process Monitoring in the Big Data/Industry 4.0 Era: from Detection, to Diagnosis, to Prognosis

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