rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Practical Approaches to Causal Relationship Exploration
  name:
    name part: Li, Jiuyong.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Liu, Lin.
    role:
      role term: author.
  name:
    name part: Le, Thuc Duy.
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2015
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: X, 80 p. 55 illus. online resource.
  abstract: This brief presents four practical methods to effectively explore causal relationships, which are often used for explanation, prediction and decision making in medicine, epidemiology, biology, economics, physics and social sciences. The first two methods apply conditional independence tests for causal discovery. The last two methods employ association rule mining for efficient causal hypothesis generation, and a partial association test and retrospective cohort study for validating the hypotheses. All four methods are innovative and effective in identifying potential causal relationships around a given target, and each has its own strength and weakness. For each method, a software tool is provided along with examples demonstrating its use. Practical Approaches to Causal Relationship Exploration is designed for researchers and practitioners working in the areas of artificial intelligence, machine learning, data mining, and biomedical research. The material also benefits advanced students interested in causal relationship discovery.
  table of contents: Introduction -- Local causal discovery with a simple PC algorithm -- A local causal discovery algorithm for high dimensional data -- Causal rule discovery with partial association test -- Causal rule discovery with cohort studies -- Experimental comparison and discussions.
  note: by Jiuyong Li, Lin Liu, Thuc Duy Le.
  subject:
    topic: Computer science
  subject:
    topic: Data mining
  subject:
    topic: Artificial intelligence
  subject:
    topic: Computer Science
  subject:
    topic: Artificial Intelligence (incl. Robotics)
  subject:
    topic: Data Mining and Knowledge Discovery
  classification: Q334-342
  classification: TJ210.2-211.495
  classification: 006.3
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: SpringerBriefs in Electrical and Computer Engineering
  identifier: 9783319144337
  identifier: http://dx.doi.org/10.1007/978-3-319-14433-7
  record info:
    record creation date: 150302
    record change date: 20150302115850.0
    record identifier: 978-3-319-14433-7