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According to some social psychologists, people tend to overemphasize internal factors as explanations—or attributions—for the behavior of other people. They tend to assume that the behavior of another person is a trait of that person, and to underestimate the power of the situation on the behavior of others. They tend to fail to recognize when the behavior of another is due to situational variables, and thus to the person’s state. This erroneous assumption is...
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While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a confounding variable, is actually causing the systematic movement in our variables of interest. For instance, as sales in ice cream increase, so does the overall rate of crime. Is it possible that indulging in your favorite flavor of ice cream could send you on a crime spree? Or, after committing crime do you think you might decide to treat yourself to a cone?
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Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
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在数据驱动的地球科学发现中,强有力的因果关系和错误的归因.

Elizabeth Eldhose1, Auroop R Ganguly2,3,4, Snigdhansu Chatterjee5

  • 1Indian Institute of Technology Bombay, Department of Civil Engineering, Mumbai, India.

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地球科学中的因果发现需要强大的方法. 我们引入了因果分析虚假性测试 (CAST),以过不可靠的链接并增强数据驱动的因果推断,确保更可靠的气候和生态政策.

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科学领域:

  • 地球和环境科学 地球和环境科学
  • 数据科学是数据科学.
  • 因果推理的原因推理.

背景情况:

  • 因果和归因研究对于地球科学发现和政策制定至关重要.
  • 目前的数据驱动方法,包括转移 (TE),由于复杂性和估计不准确性,风险是虚假的因果关系.
  • 基于物理学的方法至关重要,以避免将相关性与因果关系混为一谈.

研究的目的:

  • 解决地球科学中现有的因果发现方法的局限性.
  • 引入一个新的框架,CAST (因果分析虚假性测试),用于量化推断的因果关系的稳定性.
  • 提高数据驱动因果发现的可靠性,特别是基于TE的方法.

主要方法:

  • 开发了CAST,一个基于子样本的整体框架.
  • 使用CAST指数量化推断的因果关系的稳定性.
  • 在各种系统动态中进行了广泛的模拟,并应用于现实世界的气候数据集.

主要成果:

  • 通过数据驱动方法识别的不可靠的因果关系,CAST有效地过了这些因果关系.
  • 该框架成功地保持了真正的因果关系.
  • 通过模拟和现实世界的气候数据应用,证明了CAST的有效性.

结论:

  • 强调在因果发现中需要基于一致性的评估.
  • 在地球科学中,CAST提供了一个可泛化的策略,以提高因果推理的可靠性.
  • 强调了强有力的方法的重要性,以告知气候,生态和水政策.