Causality

JUDEA PEARL AND DANA MACKENZIE
THE BOOK OF WHY: THE NEW SCIENCE OF CAUSE AND EFFECT

New York: Basic Books, Published May 15, 2018

CausalityCausality manipulation

Causality Meaning

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Causality Script Writing

  1. Publisher's Description
  2. Authors' Bios
    • Dana Mackenzie
  3. Scientific Background
    • Excerpts for the curious
      • Chapter 2
    • Errata and updates (last revised: 5.20.21)
      Additional errata in UK edition (last revised: 12.18.18)
    • Reviews and interviews
      • Il Sole 24 Ore - Domenica (Sunday Cultural Suplement), January 6th 2019.[Italian][English]
      • 'Why,' The Times Literary Supplement, by Tristan Quinn, September 19, 2018.
      • 'The Causal Revolutionary, 3:AM Magazine, by Richard Marshall, September 8, 2018.
      • 'AI Can't Reason Why,' Wall Street Journal, by Judea Pearl and Dana Mackenzie, May 18, 2018 (WSJ subscribers, Text)
      • 'To Build Truly Intelligent Machines, Teach Tem Cause and Effect,' Quanta Magazine, by Kevin Hartnett, May 15, 2018
      • 'Artificial intelligence pioneer's new book examines the science of cause and effect,' UCLA Newsroom, Matthew Chin, May 09, 2018
    • Awards and Recognition
        Best Science Books 2018

Causality can also be crucial to dealing with adversarial attacks, subtle manipulations that force machine learning systems to fail in unexpected ways. “These attacks clearly constitute violations of the i.i.d. Assumption that underlies statistical machine learning,” the authors of the paper write, adding that adversarial vulnerabilities. Causality Causality refers to the relationship between events where one set of events (the effects) is a direct consequence of another set of events (the causes). Causal inference is the process by which one can use data to make claims about causal relationships.