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第13行： + + + + + + + + − − 在科学哲学中，因果模型(或结构因果模型)是描述系统因果机制的概念模型。因果模型可以通过提供清晰的规则来决定需要考虑/控制哪些自变量，从而改进研究设计。 − − 它们可以从现有的观察数据中回答一些问题，而无需进行随机对照试验似的干预性研究。一些干预性研究由于伦理或实践的原因是不合适的，这意味着如果没有一个因果模型，一些假设无法被检验。 − − 因果模型可以帮助解决外部有效性问题(一项研究的结果是否适用于未研究的总体)。因果模型可以允许多个研究的数据(在某些情况下)合并来回答任何单个数据集都无法回答的问题。 − − 因果模型是可证伪的，因为如果它们与数据不匹配，它们就必须作为无效模型而被拒绝。它们还必须使得接触模型所要解释现象的群体信赖它们。 第54行：
第54行： − − 亚里斯多德定义了因果关系的分类法，包括质料因、形式因、动力因、目的因。休谟更偏爱反事实，他拒绝了亚里士多德的分类法。有段时间，他甚至否认物体本身具有使得一个物体成为原因而另一个物体成为结果的“力量”。后来，他接收了“第一物体，第二个根本不存在”（“不过，对于”因果关系）。[5]：265 − − 在19世纪末，统计学学科开始形成。经过多年努力确定诸如生物遗传等领域的因果规则后，高尔顿引入了均值回归的概念（体育运动中二年级低迷使之退步），后来将他引向了非因果的相关性概念。[5] 第86行：
第82行： − + − Pearl's causal metamodel involves a three-level abstraction he calls the ladder of causation. The lowest level, Association (seeing/observing), entails the sensing of regularities or patterns in the input data, expressed as correlations. The middle level, Intervention (doing), predicts the effects of deliberate actions, expressed as causal relationships. The highest level, Counterfactuals (imagining), involves constructing a theory of (part of) the world that explains why specific actions have specific effects and what happens in the absence of such actions.+ − Pearl 的因果元模型包含一个三级抽象，他称之为因果阶梯。最低层，联想(观察/观察) ，需要感知输入数据中的规律或模式，表示为相关性。中间层次，干预(行动) ，预测故意行为的效果，表达为因果关系。最高层次的反事实(想象)包括构建一个(部分)世界的理论，解释为什么具体的行动有特定的效果，以及在没有这些行动的情况下会发生什么。
无编辑摘要
In philosophy of science, a causal model (or structural causal model) is a conceptual model that describes the causal mechanisms of a system. Causal models can improve study designs by providing clear rules for deciding which independent variables need to be included/controlled for.
In philosophy of science, a causal model (or structural causal model) is a conceptual model that describes the causal mechanisms of a system. Causal models can improve study designs by providing clear rules for deciding which independent variables need to be included/controlled for.
在科学哲学中，因果模型(或结构因果模型)是描述系统因果机制的概念模型。因果模型可以通过提供清晰的规则来决定需要考虑/控制哪些自变量，从而改进研究设计。
They can allow some questions to be answered from existing observational data without the need for an interventional study such as a [[randomized controlled trial]]. Some interventional studies are inappropriate for ethical or practical reasons, meaning that without a causal model, some hypotheses cannot be tested.
They can allow some questions to be answered from existing observational data without the need for an interventional study such as a [[randomized controlled trial]]. Some interventional studies are inappropriate for ethical or practical reasons, meaning that without a causal model, some hypotheses cannot be tested.
They can allow some questions to be answered from existing observational data without the need for an interventional study such as a randomized controlled trial. Some interventional studies are inappropriate for ethical or practical reasons, meaning that without a causal model, some hypotheses cannot be tested.
They can allow some questions to be answered from existing observational data without the need for an interventional study such as a randomized controlled trial. Some interventional studies are inappropriate for ethical or practical reasons, meaning that without a causal model, some hypotheses cannot be tested.
它们可以从现有的观察数据中回答一些问题，而无需进行随机对照试验似的干预性研究。一些干预性研究由于伦理或实践的原因是不合适的，这意味着如果没有一个因果模型，一些假设无法被检验。
Causal models can help with the question of ''external validity'' (whether results from one study apply to unstudied populations). Causal models can allow data from multiple studies to be merged (in certain circumstances) to answer questions that cannot be answered by any individual data set.
Causal models can help with the question of ''external validity'' (whether results from one study apply to unstudied populations). Causal models can allow data from multiple studies to be merged (in certain circumstances) to answer questions that cannot be answered by any individual data set.
Causal models can help with the question of external validity (whether results from one study apply to unstudied populations). Causal models can allow data from multiple studies to be merged (in certain circumstances) to answer questions that cannot be answered by any individual data set.
Causal models can help with the question of external validity (whether results from one study apply to unstudied populations). Causal models can allow data from multiple studies to be merged (in certain circumstances) to answer questions that cannot be answered by any individual data set.
因果模型可以帮助解决外部有效性问题(一项研究的结果是否适用于未研究的总体)。因果模型可以允许多个研究的数据(在某些情况下)合并来回答任何单个数据集都无法回答的问题。
Causal models are ''falsifiable'', in that if they do not match data, they must be rejected as invalid. They must also be credible to those close to the phenomena the model intends to explain.<ref>{{Cite journal|last1=Barlas|first1=Yaman|last2=Carpenter|first2=Stanley|date=1990|title=Philosophical roots of model validation: Two paradigms|journal=System Dynamics Review|language=en|volume=6|issue=2|pages=148–166|doi=10.1002/sdr.4260060203}}</ref>
Causal models are ''falsifiable'', in that if they do not match data, they must be rejected as invalid. They must also be credible to those close to the phenomena the model intends to explain.<ref>{{Cite journal|last1=Barlas|first1=Yaman|last2=Carpenter|first2=Stanley|date=1990|title=Philosophical roots of model validation: Two paradigms|journal=System Dynamics Review|language=en|volume=6|issue=2|pages=148–166|doi=10.1002/sdr.4260060203}}</ref>
Causal models are falsifiable, in that if they do not match data, they must be rejected as invalid. They must also be credible to those close to the phenomena the model intends to explain.
Causal models are falsifiable, in that if they do not match data, they must be rejected as invalid. They must also be credible to those close to the phenomena the model intends to explain.
因果模型是可证伪的，因为如果它们与数据不匹配，它们就必须作为无效模型而被拒绝。它们还必须使得接触模型所要解释现象的群体信赖它们。
Causal models have found applications in [[signal processing]], [[epidemiology]] and [[machine learning]].<ref name=":0">{{harvnb|Pearl|2009}}</ref>
Causal models have found applications in [[signal processing]], [[epidemiology]] and [[machine learning]].<ref name=":0">{{harvnb|Pearl|2009}}</ref>
Causal models have found applications in signal processing, epidemiology and machine learning.
Causal models have found applications in signal processing, epidemiology and machine learning.
因果模型在信号处理、流行病学和机器学习中都有应用。
因果模型在信号处理、流行病学和机器学习中都有应用。
== History ==
== History ==
作为一个实证主义者，皮尔逊将因果的概念从许多科学中去除，他认为因果关系是一种无法证明的特殊的关联，并引入相关系数作为关联强度的度量方法。他写道: “作为运动原因的力，与作为成长原因的树神完全一样”，而因果关系只是“现代科学高深奥秘中的迷信”。皮尔森在伦敦大学学院创建了期刊“Biometrika”和生物统计学实验室，后者成为了统计学的世界领军者。[5]
作为一个实证主义者，皮尔逊将因果的概念从许多科学中去除，他认为因果关系是一种无法证明的特殊的关联，并引入相关系数作为关联强度的度量方法。他写道: “作为运动原因的力，与作为成长原因的树神完全一样”，而因果关系只是“现代科学高深奥秘中的迷信”。皮尔森在伦敦大学学院创建了期刊“Biometrika”和生物统计学实验室，后者成为了统计学的世界领军者。[5]
[[Aristotle]] defined a taxonomy of causality, including material, formal, efficient and final causes. Hume rejected Aristotle's taxonomy in favor of [[Counterfactual conditional|counterfactuals]]. At one point, he denied that objects have "powers" that make one a cause and another an effect.<ref name=":1" />Later he adopted "if the first object had not been, the second had never existed" ("[[Sine qua non|but-for]]" causation).<ref name=":1" />
[[Aristotle]] defined a taxonomy of causality, including material, formal, efficient and final causes. Hume rejected Aristotle's taxonomy in favor of [[Counterfactual conditional|counterfactuals]]. At one point, he denied that objects have "powers" that make one a cause and another an effect.<ref name=":1" />Later he adopted "if the first object had not been, the second had never existed" ("[[Sine qua non|but-for]]" causation).<ref name=":1" />
亚里斯多德定义了因果关系的分类法，包括质料因、形式因、动力因、目的因。休谟更偏爱反事实，他拒绝了亚里士多德的分类法。有段时间，他甚至否认物体本身具有使得一个物体成为原因而另一个物体成为结果的“力量”。后来，他接收了“第一物体还没存在时，第二个根本不存在”（“but-for”因果关系）。
In the late 19th century, the discipline of statistics began to form. After a years-long effort to identify causal rules for domains such as biological inheritance, [[Francis Galton|Galton]] introduced the concept of [[Regression toward the mean|mean regression]] (epitomized by the [[sophomore slump]] in sports) which later led him to the non-causal concept of [[correlation]].<ref name=":1">{{Cite book|url={{google books |plainurl=y |id=9H0dDQAAQBAJ}} |title=The Book of Why: The New Science of Cause and Effect|last1=Pearl|first1=Judea|last2=Mackenzie|first2=Dana|date=2018-05-15|publisher=Basic Books|isbn=9780465097616|language=en|author-link=Judea Pearl}}</ref>
In the late 19th century, the discipline of statistics began to form. After a years-long effort to identify causal rules for domains such as biological inheritance, [[Francis Galton|Galton]] introduced the concept of [[Regression toward the mean|mean regression]] (epitomized by the [[sophomore slump]] in sports) which later led him to the non-causal concept of [[correlation]].<ref name=":1">{{Cite book|url={{google books |plainurl=y |id=9H0dDQAAQBAJ}} |title=The Book of Why: The New Science of Cause and Effect|last1=Pearl|first1=Judea|last2=Mackenzie|first2=Dana|date=2018-05-15|publisher=Basic Books|isbn=9780465097616|language=en|author-link=Judea Pearl}}</ref>
在19世纪末，统计学学科开始形成。经过多年努力确定诸如生物遗传等领域的因果规则后，高尔顿引入了均值回归的概念（体育运动中二年级低迷使之退步），后来将他引向了非因果的相关性概念。[5]