The Experts below are selected from a list of 291 Experts worldwide ranked by ideXlab platform

Mark Peyrot - One of the best experts on this subject based on the ideXlab platform.

  • Causal Analysis theory and application
    Journal of Pediatric Psychology, 1996
    Co-Authors: Mark Peyrot
    Abstract:

    : Identified opportunities to make statistical Analysis more closely approximate the conceptual issues that are apparent in current research in pediatric psychology. The paper proceeds by explicating the logic of multivariate Analysis and its application to Causal modeling. Causal modeling techniques are discussed and the ease of employing these techniques is evaluated. Several articles from earlier issues of this journal are reviewed in terms of the application of Causal Analysis.

Ching-pao Chang - One of the best experts on this subject based on the ideXlab platform.

  • Causal Analysis using program patterns
    2010 International Computer Symposium (ICS2010), 2010
    Co-Authors: Ko-li Cheng, Ching-pao Chang
    Abstract:

    Identifying the causes of problem during software development process is an important activity to improve the software quality. Causal Analysis is an efficient way used to identify the cause of problem. The difficulty of Causal Analysis is how to classify the causes of problem and larger effort is required to find the real problem in massive possible causes. In this paper, we propose an approach that applying program patterns on Causal Analysis process to reduce the efforts of identifying problems. The advantage of the proposed approach is that the program patterns provide software engineer efficient way to understand where the problems and provide expert the basis of solving the problem quantification which is clear and specific target.

  • Improvement of Causal Analysis using multivariate statistical process control
    Software Quality Journal, 2008
    Co-Authors: Ching-pao Chang
    Abstract:

    Statistical process control (SPC) is a conventional means of monitoring software processes and detecting related problems, where the causes of detected problems can be identified using Causal Analysis. Determining the actual causes of reported problems requires significant effort due to the large number of possible causes. This study presents an approach to detect problems and identify the causes of problems using multivariate SPC. This proposed method can be applied to monitor multiple measures of software process simultaneously. The measures which are detected as the major impacts to the out-of-control signals can be used to identify the causes where the partial least squares (PLS) and statistical hypothesis testing are utilized to validate the identified causes of problems in this study. The main advantage of the proposed approach is that the correlated indices can be monitored simultaneously to facilitate the Causal Analysis of a software process.

Masahito Kurihara - One of the best experts on this subject based on the ideXlab platform.

  • Exploratory Causal Analysis of Open Data: Explanation Generation and Confounder Identification
    Journal of Advanced Computational Intelligence and Intelligent Informatics, 2020
    Co-Authors: Jing Song, Satoshi Oyama, Masahito Kurihara
    Abstract:

    Open data are becoming increasingly available in various domains, and many organizations rely on making decisions according to data. Such decision making requires care to distinguish between correlations and Causal relationships. Among data Analysis tasks, Causal relationship Analysis is especially complex because of unobserved confounders. For example, to correctly analyze the Causal relationship between two variables, the possible confounding effect of a third variable should be considered. In the open-data environment, however, it is difficult to consider all possible confounders in advance. In this paper, we propose a framework for exploratory Causal Analysis of open data, in which possible confounding variables are collected and incrementally tested from a large volume of open data. To the extent of the authors’ knowledge, no framework has been proposed to incorporate data for possible confounders in Causal Analysis process. This paper shows an original way to expand Causal structures and generate reasonable Causal relationships. The proposed framework accounts for the effect of possible confounding in Causal Analysis by first using a crowdsourcing platform to collect explanations of the correlation between variables. Keywords are then extracted using natural language processing methods. The framework searches the related open data according to the extracted keywords. Finally, the collected explanations are tested using several automated Causal Analysis methods. We conducted experiments using open data from the World Bank and the Japanese government. The experimental results confirmed that the proposed framework enables Causal Analysis while considering the effects of possible confounders.

  • SMC - A Framework for Crowd-Based Causal Analysis of Open Data
    2018 IEEE International Conference on Systems Man and Cybernetics (SMC), 2018
    Co-Authors: Jing Song, Satoshi Oyama, Masahito Kurihara
    Abstract:

    Many organizations provide open data, and important insights can be gained by analyzing such data. Analysis of the potential Causal relationships is a complex task. We have developed a framework for analyzing Causal relationships that combines the intelligence of the crowd with state-of-the-art machine learning methods. The proposed framework takes into account the effect of possible confounding in Causal Analysis by collecting explanations of the correlation between variables. The validity of the collected explanations is tested using a Causal discovery workflow including a conditional independence test step and a Causal direction inference step. Application of this framework to data obtained from the World Bank Data website and open government data sources revealed several interesting Causal relationships. The results demonstrate that the proposed framework can efficiently perform Causal Analysis of open data.

David N. Card - One of the best experts on this subject based on the ideXlab platform.

  • Myths and Strategies of Defect Causal Analysis
    2020
    Co-Authors: David N. Card
    Abstract:

    The popular process improvement approaches (e.g., Six Sigma, CMMI, and Lean) all incorporate Causal Analysis activities. While the techniques used in Causal Analysis are well known, the concept of Causality itself often is misunderstood and misapplied. The article explores the common misunderstandings and suggests some strategies for applying Causal Analysis more effectively. It does not provide a tutorial on any specific Causal Analysis technique. Many different processes, tools, and techniques (e.g., Failure Mode Effects Analysis, Ishikawa diagrams, Pareto charts) have been developed for defect Causal Analysis. All of them have proven to be successful in some situations. Organizations often invest large amounts in the software and training needed to deploy them. While application of these techniques helps gain insight into the sources of problems, a checklist implementation of the techniques alone is not sufficient to ensure accurate identification and effective resolution of “deep” problems. Gaining a true understanding of concepts underlying Causality and developing a strategy for applying Causal Analysis help to maximize the benefit of an organization’s investment in the tools and techniques of Causal Analysis.

  • Evidence-based guidelines to defect Causal Analysis
    IEEE Software, 2012
    Co-Authors: Marcos Kalinowski, David N. Card, Guilherme Horta Travassos
    Abstract:

    Default Causal Analysis (DCA) or defect prevention is required by higher-maturity-level software development processes such as the Brazilian Software Process Improvement Reference Model and Capability Maturity Model Integration. The authors ask and answer questions about implementing it in lower-maturity organizations. In the related web extra entitled "Evidence-Based Guidelines on Defect Causal Analysis," authors Marcos Kalinowski, David N. Card, and Guilherme H. Travassos discuss the basics of research protocol. © 2012 IEEE.

  • learning from our mistakes with defect Causal Analysis
    IEEE Software, 1998
    Co-Authors: David N. Card
    Abstract:

    Defect Causal Analysis offers a simple, low-cost method for systematically improving the quality of software produced by a team, project, or organization. DCA takes advantage of one of the most widely available types of quality information, the software problem report. This information drives a team-based technique for defect Causal Analysis. The Analysis leads to process changes that help prevent defects and ensure their early detection. The paper discusses the three principles that drive the DCA approach. It considers the impact of DCA on an organization.

  • Defect-Causal Analysis drives down error rates
    IEEE Software, 1993
    Co-Authors: David N. Card
    Abstract:

    Defect-Causal Analysis (DCA), a low-cost technique for driving down error rates in software, is discussed. The learning method used in DCA is described, and its benefits are discussed. The effect of DCA on maturity (as in the Capability Maturity model) is considered. Possibly pitfalls in using DCA are pointed out. >

Allen Mcbride - One of the best experts on this subject based on the ideXlab platform.

  • A Causal Analysis framework for land-use change and the potential role of bioenergy policy
    Land Use Policy, 2016
    Co-Authors: Rebecca A Efroymson, Keith L. Kline, Virginia H Dale, Arild Angelsen, J.w.a. Langeveld, Peter H Verburg, Allen Mcbride
    Abstract:

    We propose a Causal Analysis framework to increase understanding of land-use change (LUC) and the reliability of LUC models. This health-sciences-inspired framework can be applied to determine probable causes of LUC in the context of bioenergy. Calculations of net greenhouse gas (GHG) emissions for LUC associated with biofuel production are critical in determining whether a fuel qualifies as a biofuel or advanced biofuel category under regional (EU), national (US, UK), and state (California) regulations. Biofuel policymakers and scientists continue to discuss to what extent presumed indirect land-use change (ILUC) estimates should be included in GHG accounting for biofuel pathways. Current estimates of ILUC for bioenergy rely largely on economic simulation models that focus on Causal pathways involving global commodity trade and use coarse land-cover data with simple land classification systems. This paper challenges the application of such models to estimate global areas of LUC in the absence of Causal Analysis. The proposed Causal Analysis framework begins with a definition of the change that has occurred and proceeds to a strength-of-evidence approach that includes plausibility of relationship, completeness of Causal pathway, spatial co-occurrence, time order, analogous agents, simulation model results, and quantitative agent–response relationships. We discuss how LUC may be allocated among probable causes for policy purposes and how the application of the framework has the potential to increase the validity of LUC models and resolve controversies about ILUC, such as deforestation, and biofuels.