论文标题
因果关系在因果关系的衡量标准中普遍存在
Causal emergence is widespread across measures of causation
论文作者
论文摘要
因果出现是一种理论，即宏观可以减少因果关系中的噪声，从而导致宏观的原因更强。从那以后，首先使用有效信息确定了模型系统中的集成信息，从那以后就在整个科学的实际数据中进行了分析。但这只是这些原始措施的怪癖吗？为了回答这个问题，我们对十几种流行的因果关系进行了研究，所有因果关系都独立发展和广泛使用，并涵盖了从哲学到统计学到心理学再到遗传学的不同领域。所有这些都显示出因果出现的病例。这是因为，我们证明，因果关系的衡量标准是基于一小部分相关的“因果原始人”。独立开发的因果关系的这种合伙性表明，宏观因果关系是有关因果关系的一般事实，在科学上是可检测的，并且不是任何特定因果关系的怪癖。这一发现使出现科学在牢固的基础上，为检测复杂系统中固有的功能尺度开辟了大门，并协助进行科学建模和实验性干预措施。
Causal emergence is the theory that macroscales can reduce the noise in causal relationships, leading to stronger causes at the macroscale. First identified using the effective information and later the integrated information in model systems, causal emergence has been analyzed in real data across the sciences since. But is it simply a quirk of these original measures? To answer this question we examined over a dozen popular measures of causation, all independently developed and widely used, and spanning different fields from philosophy to statistics to psychology to genetics. All showed cases of causal emergence. This is because, we prove, measures of causation are based on a small set of related "causal primitives." This consilience of independently-developed measures of causation shows that macroscale causation is a general fact about causal relationships, is scientifically detectable, and is not a quirk of any particular measure of causation. This finding sets the science of emergence on firmer ground, opening the door for the detection of intrinsic scales of function in complex systems, as well as assisting with scientific modeling and experimental interventions.
