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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.
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.
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.
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.
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.
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。
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 ==
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]
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]
1908年，哈代和温伯格通过复活孟德尔的继承权，解决了导致高尔顿放弃因果关系的特质稳定问题。[5]
在1921年，赖特的路径分析成为因果模型和因果图的理论祖先。[6]他开发了这种方法，同时试图阐明遗传，发育和环境对豚鼠皮毛模式的相对影响。他通过证明这样的分析如何解释豚鼠出生体重，子宫内时间和产仔数之间的关系来支持他当时的观点。杰出的统计学家对这些想法的反对使他们在接下来的40年中被忽略（在动物饲养员中除外）。取而代之的是，科学家依赖于相关性，部分是受赖特（Wright）评论家（和主要统计学家）费舍尔（Fisher）的要求。[5]唯一的例外是伯克斯（Burks），他是一名学生，他于1926年首先应用路径图来表示中介影响（mediator），并断言保持常量不变会导致错误。她可能独立地发明了路线图。[5]：304
1923年，内曼（Neyman）提出了潜在结果的概念，但是直到1990年他的论文才从波兰语翻译成英语。[5]：271
1958年，考克斯（Cox）警告说，控制变量Z仅在极不可能受到自变量影响的情况下才有效。[5]：154
在1960年代，Duncan，Blalock，Goldberger等人重新发现了路径分析。邓肯在阅读布拉洛克关于路径图的著作时，想起了二十年前奥格本的一次演讲，其中提到了赖特的论文，而后者又提到了伯克斯。[5]：308
社会学家最初将因果模型称为结构方程模型，但一旦成为死记硬背的方法，它就失去了效用，导致一些从业者拒绝与因果关系的任何联系。经济学家采用了路径分析的代数部分，称其为联立方程模型。但是，经济学家仍然避免将因果意义归因于他们的方程式。[5]
赖特在发表第一篇论文60年后，根据卡林（Karlin）等人的批评，发表了一篇概述该论文的文章，该论据反对仅处理线性关系，而健壮的，无模型的数据表示方式则更具启发性。[5]
1973年，刘易斯（Lewis）提倡用因果关系（反事实）代替相关性。他提到了人类预见替代世界的能力，在这个世界中，有因没有发生，而其影响只有在其原因之后才出现。[5]：266鲁宾在1974年提出了“潜在结果”的概念，作为问因果问题的语言。[5]：269
1983年，卡特赖特（Cartwright）提出，以任何与效果“因果相关”的因素为条件，超越简单概率作为唯一指导。[5]：48
1986年，Baron和Kenny引入了检测和评估线性方程组中的中介的原理。截至2014年，他们的论文在所有时间中被引用次数排在第33位。[5]：324那年，格陵兰和罗宾斯提出了“可交换性”方法，通过考虑反事实来处理混杂问题。他们建议评估如果他们没有接受治疗会给治疗组带来什么后果，并将其结果与对照组进行比较。如果他们匹配，据说没有混淆。[5]：154
哥伦比亚大学设有因果人工智能实验室，该实验室正试图将因果建模理论与人工神经网络联系起来。[7]