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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效度问题(一项研究的结果是否适用于未研究的人群)。因果模型可以允许多个研究的数据合并(在某些情况下)来回答任何单个数据集都无法回答的问题。

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.

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.

Judea Pearl defines a causal model as an ordered triple <math>\langle U, V, E\rangle</math>, where U is a set of exogenous variables whose values are determined by factors outside the model; V is a set of endogenous variables whose values are determined by factors within the model; and E is a set of structural equations that express the value of each endogenous variable as a function of the values of the other variables in U and V.

Judea Pearl defines a causal model as an ordered triple <math>\langle U, V, E\rangle</math>, where U is a set of exogenous variables whose values are determined by factors outside the model; V is a set of endogenous variables whose values are determined by factors within the model; and E is a set of structural equations that express the value of each endogenous variable as a function of the values of the other variables in U and V.

Judea Pearl 将因果模型定义为一个有序的三元组<math>\langle U, V, E\rangle</math> ，其中 u 是一组外生变量，其值由模型外部的因素决定; v 是一组内生变量，其值由模型内部的因素决定; e 是一组结构方程，表示每个内生变量的值为 u 和 v 中其他变量值的函数。

Judea Pearl 将因果模型定义为一个有序的三元组<math>\langle U, V, E\rangle</math> ，其中 U 是一组外生变量，其值由模型外部的因素决定; V 是一组内生变量，其值由模型内部的因素决定; E 是一组结构方程，把每个内生变量的值表示为 U 和 V 中其他变量值的函数。

== History ==

== History ==

As a positivist, Pearson expunged the notion of causality from much of science as an unprovable special case of association and introduced the correlation coefficient as the metric of association. He wrote, "Force as a cause of motion is exactly the same as a tree god as a cause of growth" and that causation was only a "fetish among the inscrutable arcana of modern science". Pearson founded Biometrika and the Biometrics Lab at University College London, which became the world leader in statistics. He developed this approach while attempting to untangle the relative impacts of heredity, development and environment on guinea pig coat patterns. He backed up his then-heretical claims by showing how such analyses could explain the relationship between guinea pig birth weight, in utero time and litter size. Opposition to these ideas by prominent statisticians led them to be ignored for the following 40 years (except among animal breeders). Instead scientists relied on correlations, partly at the behest of Wright's critic (and leading statistician), Fisher.

As a positivist, Pearson expunged the notion of causality from much of science as an unprovable special case of association and introduced the correlation coefficient as the metric of association. He wrote, "Force as a cause of motion is exactly the same as a tree god as a cause of growth" and that causation was only a "fetish among the inscrutable arcana of modern science". Pearson founded Biometrika and the Biometrics Lab at University College London, which became the world leader in statistics. He developed this approach while attempting to untangle the relative impacts of heredity, development and environment on guinea pig coat patterns. He backed up his then-heretical claims by showing how such analyses could explain the relationship between guinea pig birth weight, in utero time and litter size. Opposition to these ideas by prominent statisticians led them to be ignored for the following 40 years (except among animal breeders). Instead scientists relied on correlations, partly at the behest of Wright's critic (and leading statistician), Fisher.

作为一个实证主义者，皮尔逊将因果关系的概念从许多科学中去除，作为一个无法证明的特殊关联案例，并引入相关系数作为关联度量。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生物统计学实验室和生物统计学实验室，后者成为了世界统计学的领导者。他开发了这种方法，同时试图理清遗传、发育和环境对豚鼠被毛模式的相对影响。他通过展示这些分析如何能够解释豚鼠出生体重、胎儿时期和产仔数之间的关系来支持他当时的异端主张。著名统计学家对这些观点的反对导致他们在接下来的40年里被忽视(除了动物饲养者)。相反，科学家依赖于相关性，部分原因是赖特的批评者(也是首席统计学家)费舍尔的要求。

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。他开发了这种方法，同时试图理清遗传、发育和环境对豚鼠被毛模式的相对影响。他通过展示这些分析如何能够解释豚鼠出生体重、胎儿时期和产仔数之间的关系来支持他当时的异端主张。著名统计学家对这些观点的反对导致他们在接下来的40年里被忽视(除了动物饲养者)。相反，科学家依赖于相关性，部分原因是赖特的批评者(也是首席统计学家)费舍尔的要求。