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Common frameworks for causal inference are [[structural equation modeling]] and the [[Rubin causal model]].{{citation needed|date=August 2014}}

Common frameworks for causal inference are [[structural equation modeling]] and the [[Rubin causal model]].{{citation needed|date=August 2014}}

[[Epidemiology]] studies patterns of health and disease in defined populations of [[living beings]] in order to [[infer]] causes and effects. An association between an [[Exposure (environmental hazard)|exposure]] to a putative [[risk factor]] and a disease may be suggestive of, but is not equivalent to causality because [[correlation does not imply causation]]. Historically, [[Koch's postulates]] have been used since the 19th century to decide if a microorganism was the cause of a disease. In the 20th century the [[Bradford Hill criteria]], described in 1965<ref name="bh65">{{cite journal |last=Hill |first=Austin Bradford |year=1965 |title=The Environment and Disease: Association or Causation? |journal=[[Proceedings of the Royal Society of Medicine]] |volume=58 |pages=295–300 |url=http://www.edwardtufte.com/tufte/hill |pmid=14283879 |pmc=1898525 |issue=5 |doi=10.1177/003591576505800503}}</ref> have been used to assess causality of variables outside microbiology, although even these criteria are not exclusive ways to determine causality.

[[Epidemiology]] studies patterns of health and disease in defined populations of [[living beings]] in order to [[infer]] causes and effects. An association between an [[Exposure (environmental hazard)|exposure]] to a putative [[risk factor]] and a disease may be suggestive of, but is not equivalent to causality because [[correlation does not imply causation]]. Historically, [[Koch's postulates]] have been used since the 19th century to decide if a microorganism was the cause of a disease. In the 20th century the [[Bradford Hill criteria]], described in 1965<ref name="bh65">{{cite journal |last=Hill |first=Austin Bradford |year=1965 |title=The Environment and Disease: Association or Causation? |journal=[[Proceedings of the Royal Society of Medicine]] |volume=58 |pages=295–300 |url=http://www.edwardtufte.com/tufte/hill |pmid=14283879 |pmc=1898525 |issue=5 |doi=10.1177/003591576505800503}}</ref> have been used to assess causality of variables outside microbiology, although even these criteria are not exclusive ways to determine causality.

In [[molecular epidemiology]] the phenomena studied are on a [[molecular biology]] level, including genetics, where [[biomarkers]] are evidence of cause or effects.

In [[molecular epidemiology]] the phenomena studied are on a [[molecular biology]] level, including genetics, where [[biomarkers]] are evidence of cause or effects.

A recent trend{{when|date=August 2014}} is to identify [[evidence]] for influence of the exposure on [[molecular pathology]] within diseased [[Tissue (biology)|tissue]] or cells, in the emerging interdisciplinary field of [[molecular pathological epidemiology]] (MPE).{{third-party-inline|date=August 2014}} Linking the exposure to molecular pathologic signatures of the disease can help to assess causality. {{third-party-inline|date=August 2014}} Considering the inherent nature of [[heterogeneity]] of a given disease, the unique disease principle, disease phenotyping and subtyping are trends in biomedical and [[public health]] sciences, exemplified as [[personalized medicine]] and [[precision medicine]].{{third-party-inline|date=August 2014}}

A recent trend{{when|date=August 2014}} is to identify [[evidence]] for influence of the exposure on [[molecular pathology]] within diseased [[Tissue (biology)|tissue]] or cells, in the emerging interdisciplinary field of [[molecular pathological epidemiology]] (MPE).{{third-party-inline|date=August 2014}} Linking the exposure to molecular pathologic signatures of the disease can help to assess causality. {{third-party-inline|date=August 2014}} Considering the inherent nature of [[heterogeneity]] of a given disease, the unique disease principle, disease phenotyping and subtyping are trends in biomedical and [[public health]] sciences, exemplified as [[personalized medicine]] and [[precision medicine]].{{third-party-inline|date=August 2014}}

Determination of cause and effect from joint observational data for two time-independent variables, say X and Y, has been tackled using asymmetry between evidence for some model in the directions, X → Y and Y → X. The primary approaches are based on [[Algorithmic information theory]] models and noise models.{{Citation needed|date=May 2019}}

Determination of cause and effect from joint observational data for two time-independent variables, say X and Y, has been tackled using asymmetry between evidence for some model in the directions, X → Y and Y → X. The primary approaches are based on [[Algorithmic information theory]] models and noise models.{{Citation needed|date=May 2019}}

利用两个时间独立变量 x 和 y 的联合观测数据确定因果关系，用 x → y 和 y → x 方向上某些模型的证据不对称性处理了这一问题。主要的方法是基于算法信息论模型和噪声模型。

利用两个时间独立变量 x 和 y 的联合观测数据确定因果关系，用 x → y 和 y → x 方向上某些模型的证据不对称性处理了这一问题。主要的方法是基于'''<font color = '#ff8000'>算法信息论模型</font>'''和噪声模型。

In [[statistics]] and [[economics]], causality is often tested via [[regression analysis]]. Several methods can be used to distinguish actual causality from spurious correlations. First, economists constructing regression models establish the direction of causal relation based on economic theory (theory-driven econometrics). For example, if one studies the dependency between rainfall and the future price of a commodity, then theory (broadly construed) indicates that rainfall can influence prices, but futures prices cannot make changes to the amount of rain<ref>{{Cite book|last=Simon|first=Herbert|title=Models of Discovery|publisher=Springer|year=1977|location=Dordrecht|page=52}}</ref> . Second, the [[instrumental variables]] (IV) technique may be employed to remove any reverse causation by introducing a role for other variables (instruments) that are known to be unaffected by the dependent variable. Third, economists consider time precedence to choose appropriate model specification. Given that partial correlations are symmetrical, one cannot determine the direction of causal relation based on correlations only. Based on the notion of probabilistic view on causality, economists assume that causes must be prior in time than their effects. This leads to using the variables representing phenomena happening earlier as independent variables and developing econometric tests for causality (e.g., Granger-causality tests) applicable in time series analysis<ref>{{Cite book|last=Maziarz|first=Mariusz|title=The Philosophy of Causality in Economics: Causal Inferences and Policy Proposals|publisher=Routledge|year=2020|location=New York}}</ref>. Fifth, other regressors are included to ensure that [[confounding variable]]s are not causing a regressor to appear to be significant spuriously but, in the areas suffering from the problem of multicollinearity such as macroeconomics, it is in principle impossible to include all confounding factors and therefore econometric models are susceptible to the common-cause fallacy.<ref>{{Cite journal|last=Henschen|first=Tobias|date=2018|title=The in-principle inconclusiveness of causal evidence in macroeconomics|journal=European Journal for Philosophy of Science|volume=8|pages=709–733}}</ref>. Recently, the movement of design-based econometrics has popularized using natural experiments and quasi-experimental research designs to address the problem of spurious correlations.<ref>{{Cite book|last=Angrist Joshua & Pischke Jörn-Steffen|title=Mostly Harmless Econometrics: An Empiricist's Companion|publisher=Princeton University Press|year=2008|location=Princeton}}</ref>

In [[statistics]] and [[economics]], causality is often tested via [[regression analysis]]. Several methods can be used to distinguish actual causality from spurious correlations. First, economists constructing regression models establish the direction of causal relation based on economic theory (theory-driven econometrics). For example, if one studies the dependency between rainfall and the future price of a commodity, then theory (broadly construed) indicates that rainfall can influence prices, but futures prices cannot make changes to the amount of rain<ref>{{Cite book|last=Simon|first=Herbert|title=Models of Discovery|publisher=Springer|year=1977|location=Dordrecht|page=52}}</ref> . Second, the [[instrumental variables]] (IV) technique may be employed to remove any reverse causation by introducing a role for other variables (instruments) that are known to be unaffected by the dependent variable. Third, economists consider time precedence to choose appropriate model specification. Given that partial correlations are symmetrical, one cannot determine the direction of causal relation based on correlations only. Based on the notion of probabilistic view on causality, economists assume that causes must be prior in time than their effects. This leads to using the variables representing phenomena happening earlier as independent variables and developing econometric tests for causality (e.g., Granger-causality tests) applicable in time series analysis<ref>{{Cite book|last=Maziarz|first=Mariusz|title=The Philosophy of Causality in Economics: Causal Inferences and Policy Proposals|publisher=Routledge|year=2020|location=New York}}</ref>. Fifth, other regressors are included to ensure that [[confounding variable]]s are not causing a regressor to appear to be significant spuriously but, in the areas suffering from the problem of multicollinearity such as macroeconomics, it is in principle impossible to include all confounding factors and therefore econometric models are susceptible to the common-cause fallacy.<ref>{{Cite journal|last=Henschen|first=Tobias|date=2018|title=The in-principle inconclusiveness of causal evidence in macroeconomics|journal=European Journal for Philosophy of Science|volume=8|pages=709–733}}</ref>. Recently, the movement of design-based econometrics has popularized using natural experiments and quasi-experimental research designs to address the problem of spurious correlations.<ref>{{Cite book|last=Angrist Joshua & Pischke Jörn-Steffen|title=Mostly Harmless Econometrics: An Empiricist's Companion|publisher=Princeton University Press|year=2008|location=Princeton}}</ref>

在统计学和经济学中，因果关系通常通过回归分析来检验。有几种方法可以用来区分实际的因果关系和虚假的相关性。首先，经济学家根据经济理论(理论驱动计量经济学)构建回归模型，确定因果关系的方向。例如，如果研究降雨量与商品未来价格之间的依赖关系，那么理论(广义解释)表明，降雨量可以影响价格，但期货价格不能改变降雨量。其次，工具变量(IV)技术可以用来消除任何反向因果关系，通过引入其他变量(工具)的作用，已知是不受因变量的影响。第三，经济学家考虑时间优先选择合适的模型规范。由于部分相关是对称的，人们不能确定方向的因果关系的基础上，只有相关性。基于对因果关系的概率观点，经济学家假设原因必须在时间上优先于它们的结果。这导致使用表示早期发生的现象的变量作为自变量，并开发适用于时间序列分析的因果关系检验(例如，格兰杰因果检验)的计量经济学检验。第五，包括其他回归因素是为了确保混杂变量不会导致回归因素出现明显的虚假性，但在遭受多重共线性问题困扰的领域，如宏观经济学，原则上不可能包括所有混杂因素，因此计量经济模型容易出现共因谬误。 .近年来，以设计为基础的计量经济学运动已经推广使用自然实验和准实验研究设计来解决虚假关联的问题。

在统计学和经济学中，因果关系通常通过回归分析来检验。有几种方法可以用来区分实际的因果关系和虚假的相关性。首先，经济学家根据经济理论(理论驱动计量经济学)构建回归模型，确定因果关系的方向。例如，如果研究降雨量与商品未来价格之间的依赖关系，那么理论(广义解释)表明，降雨量可以影响价格，但期货价格不能改变降雨量。其次，'''<font color = '#ff8000'>工具变量(IV)</font>'''技术可以用来消除任何反向因果关系，通过引入其他变量(工具)的作用，已知是不受因变量的影响。第三，经济学家考虑时间优先选择合适的模型规范。由于部分相关是对称的，人们不能确定方向的因果关系的基础上，只有相关性。基于对因果关系的概率观点，经济学家假设原因必须在时间上优先于它们的结果。这导致使用表示早期发生的现象的变量作为自变量，并开发适用于时间序列分析的因果关系检验(例如，'''<font color = '#ff8000'>格兰杰因果检验</font>''')的计量经济学检验。第五，包括其他回归因素是为了确保'''<font color = '#ff8000'>混淆变量</font>'''不会导致回归因素出现明显的虚假性，但在遭受多重共线性问题困扰的领域，如宏观经济学，原则上不可能包括所有混杂因素，因此计量经济模型容易出现共因谬误。 .近年来，以设计为基础的计量经济学运动已经推广使用自然实验和准实验研究设计来解决虚假关联的问题。

The social sciences have moved increasingly toward a quantitative framework for assessing causality. Much of this has been described as a means of providing greater rigor to social science methodology. Political science was significantly influenced by the publication of [[Designing Social Inquiry]], by Gary King, Robert Keohane, and Sidney Verba, in 1994. King, Keohane, and Verba (often abbreviated as KKV) recommended that researchers applying both quantitative and qualitative methods adopt the language of statistical inference to be clearer about their subjects of interest and units of analysis.<ref>{{Cite book|title=Designing social inquiry : scientific inference in qualitative research|first=Gary|last=King|date=2012|publisher=Princeton Univ. Press|isbn=978-0691034713|oclc=754613241}}</ref><ref name=":0">{{Cite journal|last=Mahoney|first=James|date=January 2010|title=After KKV|journal=World Politics|volume=62|issue=1|pages=120–147|jstor=40646193|doi=10.1017/S0043887109990220}}</ref> Proponents of quantitative methods have also increasingly adopted the [[Rubin causal model|potential outcomes framework]], developed by [[Donald Rubin]], as a standard for inferring causality.{{Citation needed|date=May 2019}}

The social sciences have moved increasingly toward a quantitative framework for assessing causality. Much of this has been described as a means of providing greater rigor to social science methodology. Political science was significantly influenced by the publication of [[Designing Social Inquiry]], by Gary King, Robert Keohane, and Sidney Verba, in 1994. King, Keohane, and Verba (often abbreviated as KKV) recommended that researchers applying both quantitative and qualitative methods adopt the language of statistical inference to be clearer about their subjects of interest and units of analysis.<ref>{{Cite book|title=Designing social inquiry : scientific inference in qualitative research|first=Gary|last=King|date=2012|publisher=Princeton Univ. Press|isbn=978-0691034713|oclc=754613241}}</ref><ref name=":0">{{Cite journal|last=Mahoney|first=James|date=January 2010|title=After KKV|journal=World Politics|volume=62|issue=1|pages=120–147|jstor=40646193|doi=10.1017/S0043887109990220}}</ref> Proponents of quantitative methods have also increasingly adopted the [[Rubin causal model|potential outcomes framework]], developed by [[Donald Rubin]], as a standard for inferring causality.{{Citation needed|date=May 2019}}

社会科学越来越倾向于一个评估因果关系的定量框架。其中很大一部分被描述为一种提供更严密的社会科学方法论的手段。1994年，加里 · 金、罗伯特 · 基奥汉和西德尼 · 维尔巴合著的《设计社会探究》对政治科学产生了重大影响。King，Keohane，和 Verba (通常缩写为 KKV)建议研究人员应用定量和定性的方法采用推论统计学的语言来更清楚地说明他们感兴趣的主题和分析的单位。定量方法的支持者也越来越多地采用唐纳德 · 鲁宾开发的潜在结果框架作为推断因果关系的标准。

社会科学越来越倾向于一个评估因果关系的定量框架。其中很大一部分被描述为一种提供更严密的社会科学方法论的手段。1994年，加里 · 金、罗伯特 · 基奥汉和西德尼 · 维尔巴合著的《'''<font color = '#ff8000'>设计社会探究</font>'''》对政治科学产生了重大影响。King，Keohane，和 Verba (通常缩写为 KKV)建议研究人员应用定量和定性的方法采用推论统计学的语言来更清楚地说明他们感兴趣的主题和分析的单位。定量方法的支持者也越来越多地采用'''<font color = '#ff8000'>唐纳德 · 鲁宾</font>'''开发的'''<font color = '#ff8000'>潜在结果框架</font>'''作为推断因果关系的标准。

Debates over the appropriate application of quantitative methods to infer causality resulted in increased attention to the reproducibility of studies. Critics of widely-practiced methodologies argued that researchers have engaged in [[Data dredging|P hacking]] to publish articles on the basis of spurious correlations.<ref>{{Cite news|url=https://www.nytimes.com/2017/10/18/magazine/when-the-revolution-came-for-amy-cuddy.html|title=When the Revolution Came for Amy Cuddy|last=Dominus|first=Susan|date=18 October 2017|work=The New York Times|access-date=2019-03-02|language=en-US|issn=0362-4331}}</ref> To prevent this, some have advocated that researchers preregister their research designs prior to conducting to their studies so that they do not inadvertently overemphasize a non-reproducible finding that was not the initial subject of inquiry but was found to be statistically significant during data analysis.<ref>{{Cite web|url=https://www.americanscientist.org/article/the-statistical-crisis-in-science|title=The Statistical Crisis in Science|date=6 February 2017|website=American Scientist|language=en|access-date=2019-04-18}}</ref> Internal debates about methodology and reproducibility within the social sciences have at times been acrimonious.{{Citation needed|date=May 2019}}

Debates over the appropriate application of quantitative methods to infer causality resulted in increased attention to the reproducibility of studies. Critics of widely-practiced methodologies argued that researchers have engaged in [[Data dredging|P hacking]] to publish articles on the basis of spurious correlations.<ref>{{Cite news|url=https://www.nytimes.com/2017/10/18/magazine/when-the-revolution-came-for-amy-cuddy.html|title=When the Revolution Came for Amy Cuddy|last=Dominus|first=Susan|date=18 October 2017|work=The New York Times|access-date=2019-03-02|language=en-US|issn=0362-4331}}</ref> To prevent this, some have advocated that researchers preregister their research designs prior to conducting to their studies so that they do not inadvertently overemphasize a non-reproducible finding that was not the initial subject of inquiry but was found to be statistically significant during data analysis.<ref>{{Cite web|url=https://www.americanscientist.org/article/the-statistical-crisis-in-science|title=The Statistical Crisis in Science|date=6 February 2017|website=American Scientist|language=en|access-date=2019-04-18}}</ref> Internal debates about methodology and reproducibility within the social sciences have at times been acrimonious.{{Citation needed|date=May 2019}}

关于适当应用定量方法来推断因果关系的争论导致了对研究重复性的更多关注。对广泛使用的方法论持批评态度的人认为，研究人员利用 p 黑客技术，在虚假关联的基础上发表文章。为了防止这种情况，一些人主张研究人员在进行研究之前预先注册他们的研究设计，这样他们就不会无意中过分强调一项不可复制的发现，这项发现并非最初的调查对象，但在数据分析中被发现具有统计意义。社会科学内部关于方法论和可重现性的争论有时是尖刻的。

关于适当应用定量方法来推断因果关系的争论导致了对研究重复性的更多关注。对广泛使用的方法论持批评态度的人认为，研究人员利用 '''<font color = '#ff8000'>p-hacking</font>'''技术，在虚假关联的基础上发表文章。为了防止这种情况，一些人主张研究人员在进行研究之前预先注册他们的研究设计，这样他们就不会无意中过分强调一项不可复制的发现，这项发现并非最初的调查对象，但在数据分析中被发现具有统计意义。社会科学内部关于方法论和可重现性的争论有时是尖刻的。

参见

参见

* [[Causal analysis]]

* [[Causal analysis]] 因果分析

* [[Granger causality]]

* [[Granger causality]] 格兰杰因果分析

* [[Multivariate statistics]]

* [[Multivariate statistics]] 多元统计

* [[Partial least squares regression]]

* [[Partial least squares regression]] 偏最小二乘回归

* [[Pathogenesis]]

* [[Pathogenesis]] 发病学

* [[Pathology]]

* [[Pathology]] 病理学

* [[Regression analysis]]

* [[Regression analysis]] 回归分析

* [[Transfer entropy]]

* [[Transfer entropy]] 传递熵