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'''Causal inference''' is the process of drawing a conclusion about a [[causal]] connection based on the conditions of the occurrence of an effect. The main difference between causal inference and inference of [[association (statistics)|association]] is that the former analyzes the response of the effect variable when the cause is changed.<ref name=Pearl_Journal>{{cite journal|last=Pearl|first=Judea|title=Causal inference in statistics: An overview|journal=Statistics Surveys|date=1 January 2009|volume=3|issue=|pages=96–146|doi=10.1214/09-SS057|url=http://ftp.cs.ucla.edu/pub/stat_ser/r350.pdf}}</ref><ref name=Morgan_book>{{cite book|last=Morgan|first=Stephen|author2=Winship, Chris|title=Counterfactuals and Causal inference|publisher=Cambridge University Press|year=2007|isbn=978-0-521-67193-4}}</ref> The science of why things occur is called [[etiology]].  Causal inference is an example of [[causal reasoning]].

'''Causal inference''' is the process of drawing a conclusion about a [[causal]] connection based on the conditions of the occurrence of an effect. The main difference between causal inference and inference of [[association (statistics)|association]] is that the former analyzes the response of the effect variable when the cause is changed.<ref name=Pearl_Journal>{{cite journal|last=Pearl|first=Judea|title=Causal inference in statistics: An overview|journal=Statistics Surveys|date=1 January 2009|volume=3|issue=|pages=96–146|doi=10.1214/09-SS057|url=http://ftp.cs.ucla.edu/pub/stat_ser/r350.pdf}}</ref><ref name=Morgan_book>{{cite book|last=Morgan|first=Stephen|author2=Winship, Chris|title=Counterfactuals and Causal inference|publisher=Cambridge University Press|year=2007|isbn=978-0-521-67193-4}}</ref> The science of why things occur is called [[etiology]].  Causal inference is an example of [[causal reasoning]].

'''<font color = '#ff8000'>因果推断Causal inference</font>'''是根据某一效应发生的条件得出关于因果关系的结论的过程。因果推理与关联推理的主要区别在于前者是分析结果变量在原因发生变化时的响应。为什么事情会发生的科学叫做'''<font color = '#ff8000'>病因学</font>'''。因果推理就是'''<font color = '#ff8000'>因果推理</font>'''推理的一个例子。

'''<font color = '#ff8000'>因果推断Causal inference</font>'''是根据某一效应发生的条件得出关于因果关系的结论的过程。因果推断与'''<font color = '#ff8000'>关联推理inference of association</font>'''的主要区别在于前者分析在原因变化时结果变量的反应。研究事情为什么发生的科学叫做'''<font color = '#ff8000'>病因学etiology</font>'''。因果推断是'''<font color = '#ff8000'>因果推理causal reasoning</font>'''的一个例子。

==Definition==

==Definition定义==

 

Inferring the [[cause]] of something has been described as:

Inferring the [[cause]] of something has been described as:

推断某事的起因被描述为:

'''<font color = '#32CD32'>对某事原因的推断过程已经被描述为是:Inferring the [[cause]] of something has been described as:</font>'''

 

 

*"...reason[ing] to the conclusion that something is, or is likely to be, the cause of something else".<ref name=EB>{{cite web|title=causal inference|url=http://www.britannica.com/EBchecked/topic/1442615/causal-inference|publisher=Encyclopædia Britannica, Inc.|accessdate=24 August 2014}}</ref>

*"...reason[ing] to the conclusion that something is, or is likely to be, the cause of something else".<ref name=EB>{{cite web|title=causal inference|url=http://www.britannica.com/EBchecked/topic/1442615/causal-inference|publisher=Encyclopædia Britannica, Inc.|accessdate=24 August 2014}}</ref>

*"Identification of the cause or causes of a phenomenon, by establishing covariation of cause and effect, a time-order relationship with the cause preceding the effect, and the elimination of plausible alternative causes."<ref name=psy>{{cite book|author1=John Shaughnessy|author2=Eugene Zechmeister|author3=Jeanne Zechmeister|title=Research Methods in Psychology|date=2000|publisher=McGraw-Hill Humanities/Social Sciences/Languages|isbn=978-0077825362|pages=Chapter 1 : Introduction|url=http://www.mhhe.com/socscience/psychology/shaugh/ch01_concepts.html|accessdate=24 August 2014|archive-url=https://web.archive.org/web/20141015135541/http://www.mhhe.com/socscience/psychology/shaugh/ch01_concepts.html|archive-date=15 October 2014|url-status=dead}}</ref>

*"Identification of the cause or causes of a phenomenon, by establishing covariation of cause and effect, a time-order relationship with the cause preceding the effect, and the elimination of plausible alternative causes."<ref name=psy>{{cite book|author1=John Shaughnessy|author2=Eugene Zechmeister|author3=Jeanne Zechmeister|title=Research Methods in Psychology|date=2000|publisher=McGraw-Hill Humanities/Social Sciences/Languages|isbn=978-0077825362|pages=Chapter 1 : Introduction|url=http://www.mhhe.com/socscience/psychology/shaugh/ch01_concepts.html|accessdate=24 August 2014|archive-url=https://web.archive.org/web/20141015135541/http://www.mhhe.com/socscience/psychology/shaugh/ch01_concepts.html|archive-date=15 October 2014|url-status=dead}}</ref>

Epidemiological studies employ different [[epidemiological method]]s of collecting and measuring evidence of risk factors and effect and different ways of measuring association between the two. A [[hypothesis]] is formulated, and then [[Statistical hypothesis testing|tested with statistical methods]]. It is [[statistical inference]] that helps decide if data are due to chance, also called [[random variation]], or indeed correlated and if so how strongly. However, [[correlation does not imply causation]], so further methods must be used to infer causation.{{Citation needed|date=May 2019}}

Epidemiological studies employ different [[epidemiological method]]s of collecting and measuring evidence of risk factors and effect and different ways of measuring association between the two. A [[hypothesis]] is formulated, and then [[Statistical hypothesis testing|tested with statistical methods]]. It is [[statistical inference]] that helps decide if data are due to chance, also called [[random variation]], or indeed correlated and if so how strongly. However, [[correlation does not imply causation]], so further methods must be used to infer causation.{{Citation needed|date=May 2019}}

流行病学研究采用不同的'''<font color = '#ff8000'>流行病学</font>'''方法来收集和衡量危险因素和影响的证据，并采用不同的方法来衡量两者之间的关联性。一个假设被制定出来，然后用统计学方法进行检验。这个'''<font color = '#ff8000'>推论统计学</font>'''有助于判断数据是由偶然性引起的，也称为'''<font color = '#ff8000'>随机变异</font>'''，还是确实存在相关性，以及相关性有多强。然而，相关不蕴涵因果，因此必须使用进一步的方法来推断因果关系。

'''<font color = '#ff8000'>流行病学epidemiological</font>'''收集和衡量危险因素和结果，以及衡量危险因素和结果之间关系的方法与其他学科不同。一个假设提出来以后用'''<font color = '#ff8000'>统计学假设检验Statistical hypothesis testing</font>'''。这种'''<font color = '#ff8000'>统计学推断statistical inference </font>'''有助于判断数据是由偶然性引起的，也就是'''<font color = '#ff8000'>随机变异random variation</font>'''，还是确实存在相关性，以及相关性有多强。然而，相关不意味着因果，因此必须进一步使用其他方法来推断因果关系。

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}}

因果推理的常见框架有'''<font color = '#ff8000'>结构方程模型</font>'''和'''<font color = '#ff8000'>虚拟事实模型</font>'''。

因果推断的常见框架有'''<font color = '#ff8000'>结构方程模型structural equation modeling</font>'''和'''<font color = '#ff8000'>Rubin因果模型 Rubin causal model</font>'''。

==In epidemiology==

==In epidemiology 在流行病学中==

 

[[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.

'''<font color = '#ff8000'>流行病学</font>'''研究特定生物群体的健康和疾病模式，以推断原因和结果。暴露于一个推定的'''<font color = '#ff8000'>危险因素</font>'''和疾病之间的联系可能是暗示性的，但不等同于因果关系，因为相关不蕴涵因果。从历史上看，'''<font color = '#ff8000'>科赫的假设</font>'''从19世纪开始就被用来判断一种微生物是否是一种疾病的原因。在20世纪，1965年描述的'''<font color = '#ff8000'>布拉德福德·希尔准则</font>'''已经被用来评估微生物学之外的变量的因果关系，尽管这些标准并不是确定因果关系的唯一方法。

'''<font color = '#ff8000'>流行病学Epidemiology</font>'''研究特定生物群体的健康和疾病'''<font color = '#ff8000'>模式patterns</font>'''，以推断原因和结果。'''<font color = '#ff8000'>暴露exposure </font>'''于一般认为的'''<font color = '#ff8000'>危险因素risk factor</font>'''和疾病之间的联系可能被提出，但不等同于确认因果关系，因为相关不意味着因果。从历史上看，'''<font color = '#ff8000'>科赫法则 Koch's postulates</font>'''从19世纪开始就被用来判断一种微生物是否是一种疾病的原因。在20世纪，'''<font color = '#ff8000'>布拉德福德·希尔准则Bradford Hill criteria</font>'''（参见Bradford Hill 1965年的文章<ref name="bh65"></ref>中）已经被用来评估微生物学之外的变量的因果关系，尽管即使是这些标准也不是确定因果关系的唯一方法。

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.

在21'''<font color = '#ff8000'>分子流行病学</font>'''，研究的现象是在'''<font color = '#ff8000'>分子生物学</font>'''水平上，包括遗传学，其中'''<font color = '#ff8000'>生物标志物</font>'''是原因或影响的证据。

'''<font color = '#ff8000'>分子流行病学molecular epidemiology</font>'''中研究的现象是在'''<font color = '#ff8000'>分子生物学</font>'''水平上的，也涵盖了遗传学。而遗传学中的'''<font color = '#ff8000'>生物标志物biomarkers</font>'''就是原因或结果的证据。

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}}

在新兴的分子病理流行病学交叉学科领域，最近的一个趋势是确定暴露对病变组织或细胞内'''<font color = '#ff8000'>分子病理学</font>'''的影响的证据。将暴露与疾病的分子病理特征联系起来可以帮助评估因果关系。考虑到给定疾病的内在'''<font color = '#ff8000'>异质性</font>'''，独特的疾病原理，疾病表型分型和亚型是生物医学和'''<font color = '#ff8000'>公共卫生科学</font>'''的趋势，例如'''<font color = '#ff8000'>个体化医学</font>'''和'''<font color = '#ff8000'>精准医学</font>'''。

在新兴的'''<font color = '#ff8000'>分子病理流行病学molecular pathological epidemiology (MPE)</font>'''交叉学科领域，最近的一个趋势（截至2014年）是确定在病变组织或细胞内，'''<font color = '#ff8000'>暴露exposure </font>'''在'''<font color = '#ff8000'>分子病理学molecular pathology</font>'''上的影响。将暴露与疾病的分子病理特征联系起来可以帮助因果关系的评估。鉴于给定疾病的'''<font color = '#ff8000'>异质性</font>'''的固有特征、独特的疾病原理等，疾病'''<font color = '#ff8000'>表现型phenotyping</font>'''和'''<font color = '#ff8000'>亚型subtyping </font>'''现在是生物医学和'''<font color = '#ff8000'>公共卫生科学public health</font>'''的趋势，例证包括'''<font color = '#ff8000'>个体化医学personalized medicine</font>'''和'''<font color = '#ff8000'>精准医学precision medicine</font>'''等。

 

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'''<font color = '#ff8000'>算法信息论模型</font>'''和噪声模型。

确定两个时间独立变量：X 和 Y 的联合观测数据因果关系的问题已经被解决了，方法是利用 X → Y 和 Y → X 方向上某些模型的证据不对称性。主要的方法基于'''<font color = '#ff8000'>算法信息理论Algorithmic information theory</font>'''模型和'''<font color = '#ff8000'>噪声模型noise models</font>'''。

While much of the emphasis remains on statistical inference in the potential outcomes framework, social science methodologists have developed new tools to conduct causal inference with both qualitative and quantitative methods, sometimes called a “mixed methods” approach.<ref>{{Cite book|url=https://books.google.com/books/about/Designing_and_Conducting_Mixed_Methods_R.html?id=YcdlPWPJRBcC|title=Designing and Conducting Mixed Methods Research|last=Creswell|first=John W.|last2=Clark|first2=Vicki L. Plano|date=2011|publisher=SAGE Publications|isbn=9781412975179|language=en}}</ref><ref>{{Cite book|url=https://www.cambridge.org/core/books/multimethod-social-science/286C2742878FBCC6225E2F10D6095A0C|title=Multi-Method Social Science by Jason Seawright|last=Seawright|first=Jason|date=September 2016|website=Cambridge Core|language=en|access-date=2019-04-18|doi=10.1017/CBO9781316160831|isbn=9781316160831}}</ref> Advocates of diverse methodological approaches argue that different methodologies are better suited to different subjects of study. Sociologist Herbert Smith and Political Scientists James Mahoney and Gary Goertz have cited the observation of Paul Holland, a statistician and author of the 1986 article “Statistics and Causal Inference,” that statistical inference is most appropriate for assessing the “effects of causes” rather than the “causes of effects.”<ref>{{Cite journal|last=Smith|first=Herbert L.|date=10 February 2014|title=Effects of Causes and Causes of Effects: Some Remarks from the Sociological Side|journal=Sociological Methods and Research|volume=43|issue=3|pages=406–415|doi=10.1177/0049124114521149|pmid=25477697|pmc=4251584}}</ref><ref>{{Cite journal|last=Goertz|first=Gary|last2=Mahoney|first2=James|date=2006|title=A Tale of Two Cultures: Contrasting Quantitative and Qualitative Research|journal=Political Analysis|language=en|volume=14|issue=3|pages=227–249|doi=10.1093/pan/mpj017|issn=1047-1987}}</ref> Qualitative methodologists have argued that formalized models of causation, including process tracing and fuzzy set theory, provide opportunities to infer causation through the identification of critical factors within case studies or through a process of comparison among several case studies.<ref name=":0" /> These methodologies are also valuable for subjects in which a limited number of potential observations or the presence of confounding variables would limit the applicability of statistical inference.{{Citation needed|date=May 2019}}

While much of the emphasis remains on statistical inference in the potential outcomes framework, social science methodologists have developed new tools to conduct causal inference with both qualitative and quantitative methods, sometimes called a “mixed methods” approach.<ref>{{Cite book|url=https://books.google.com/books/about/Designing_and_Conducting_Mixed_Methods_R.html?id=YcdlPWPJRBcC|title=Designing and Conducting Mixed Methods Research|last=Creswell|first=John W.|last2=Clark|first2=Vicki L. Plano|date=2011|publisher=SAGE Publications|isbn=9781412975179|language=en}}</ref><ref>{{Cite book|url=https://www.cambridge.org/core/books/multimethod-social-science/286C2742878FBCC6225E2F10D6095A0C|title=Multi-Method Social Science by Jason Seawright|last=Seawright|first=Jason|date=September 2016|website=Cambridge Core|language=en|access-date=2019-04-18|doi=10.1017/CBO9781316160831|isbn=9781316160831}}</ref> Advocates of diverse methodological approaches argue that different methodologies are better suited to different subjects of study. Sociologist Herbert Smith and Political Scientists James Mahoney and Gary Goertz have cited the observation of Paul Holland, a statistician and author of the 1986 article “Statistics and Causal Inference,” that statistical inference is most appropriate for assessing the “effects of causes” rather than the “causes of effects.”<ref>{{Cite journal|last=Smith|first=Herbert L.|date=10 February 2014|title=Effects of Causes and Causes of Effects: Some Remarks from the Sociological Side|journal=Sociological Methods and Research|volume=43|issue=3|pages=406–415|doi=10.1177/0049124114521149|pmid=25477697|pmc=4251584}}</ref><ref>{{Cite journal|last=Goertz|first=Gary|last2=Mahoney|first2=James|date=2006|title=A Tale of Two Cultures: Contrasting Quantitative and Qualitative Research|journal=Political Analysis|language=en|volume=14|issue=3|pages=227–249|doi=10.1093/pan/mpj017|issn=1047-1987}}</ref> Qualitative methodologists have argued that formalized models of causation, including process tracing and fuzzy set theory, provide opportunities to infer causation through the identification of critical factors within case studies or through a process of comparison among several case studies.<ref name=":0" /> These methodologies are also valuable for subjects in which a limited number of potential observations or the presence of confounding variables would limit the applicability of statistical inference.{{Citation needed|date=May 2019}}

虽然在潜在结果框架中，大部分重点仍然放在推论统计学上，但社会科学方法论者已经开发出新的工具，用定性和定量的方法进行因果推理，有时被称为混合方法。不同方法论的支持者认为不同的方法论更适合不同的研究对象。社会学家 Herbert Smith 和政治学家 James Mahoney 和 Gary Goertz 引用了统计学家 Paul Holland 的观察结果，他在1986年发表了一篇名为《统计学和因果推断》的文章，认为推论统计学最适合于评估“原因的影响”而不是“影响的原因” 定性方法学家认为，形式化的因果关系模型，包括过程追踪和模糊集合理论，提供了推断因果关系的机会，通过在案例研究中识别关键因素或通过几个案例研究之间的比较过程。这些方法对于那些数量有限的潜在观察或混杂变量的存在会限制推论统计学的适用性的课题也是有价值的。

虽然在潜在结果框架中，大部分重点仍然放在推论统计学上，但社会科学方法论者已经开发出新的工具，用定性和定量的方法进行因果推断，有时被称为混合方法。不同方法论的支持者认为不同的方法论更适合不同的研究对象。社会学家 Herbert Smith 和政治学家 James Mahoney 和 Gary Goertz 引用了统计学家 Paul Holland 的观察结果，他在1986年发表了一篇名为《统计学和因果推断》的文章，认为推论统计学最适合于评估“原因的影响”而不是“影响的原因” 定性方法学家认为，形式化的因果关系模型，包括过程追踪和模糊集合理论，提供了推断因果关系的机会，通过在案例研究中识别关键因素或通过几个案例研究之间的比较过程。这些方法对于那些数量有限的潜在观察或混杂变量的存在会限制推论统计学的适用性的课题也是有价值的。