Cause Effect Pairs in Machine Learning

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372 pages

Book Description

This book presents ground-breaking advances in the domain of causal structure learning. The problem of distinguishing cause from effect (“Does altitude cause a change in atmospheric pressure, or vice versa?”) is here cast as a binary classification problem, to be tackled by algorithms.  Based on the results of the ChaLearn Cause-Effect Pairs Challenge, this book reveals that the joint of two variables can be scrutinized by machine learning algorithms to reveal the possible existence of a “causal mechanism”, in the sense that the of one variable may have been generated from the of the other.

This book provides both tutorial material on the state-of-the-art on cause-effect pairs and exposes the reader to more advanced material, with a collection of selected papers. Supplemental material includes videos, slides, and which can be found on the workshop website.

Discovering causal relationships from observational data will become increasingly important in data science with the increasing amount of available data, as a means of detecting potential triggers in epidemiology, social sciences, , , medicine, and other sciences.

Book Details

  • Title: Cause Effect Pairs in Machine Learning
  • Length: 372 pages
  • Edition: 1st ed. 2019
  • Language: English
  • Publisher:
  • Publication Date: 2019-12-19
  • ISBN-10: 3030218090
  • ISBN-13: 9783030218096
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