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{{short description|Branch of statistics concerned with inferring causal relationships between variables}}
{{Expert needed|date=October 2019}}
'''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]].
因果推理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病因学。因果推理就是因果推理推理的一个例子。
==Definition==
定义
Inferring the [[cause]] of something has been described as:
推断某事的起因被描述为:
*"...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>
==Methods==
方法
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流行病学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。这个推论统计学有助于判断数据是由偶然性引起的，也称为随机变异，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。
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结构方程模型和虚拟事实模型。
==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.
流行病学研究特定生物群体的健康和疾病模式，以推断原因和结果。暴露于一个推定的危险因素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，科赫的假设从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年描述的布拉德福德·希尔准则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。
In [[molecular epidemiology]] the phenomena studied are on a [[molecular biology]] level, including genetics, where [[biomarkers]] are evidence of cause or effects.
在21分子流行病学，研究的现象是在分子生物学水平上，包括遗传学，其中生物标志物是原因或影响的证据。
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}}
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分子病理学的影响的证据。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异质性，独特的疾病原理，疾病表型分型和亚型是生物医学和公共卫生科学的趋势，例如个体化医学和精准医学。
==In computer science==
在计算机科学领域
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。
===Algorithmic information models===
算法信息模型
Compare two programs, both of which output both X and Y.
比较两个同时输出 x 和 y 的程序。
* Store Y and a compressed form of X in terms of uncompressed Y.
* Store X and a compressed form of Y in terms of uncompressed X.
The shortest such program implies the uncompressed stored variable more-likely causes the computed variable.<ref>Kailash Budhathoki and Jilles Vreeken "[http://eda.mmci.uni-saarland.de/pubs/2016/origo-budhathoki,vreeken.pdf Causal Inference by Compression]" 2016 IEEE 16th International Conference on Data Mining (ICDM)</ref><ref>{{Cite journal |doi = 10.1007/s10115-018-1286-7|title = Telling cause from effect by local and global regression|journal = Knowledge and Information Systems|year = 2018|last1 = Marx|first1 = Alexander|last2 = Vreeken|first2 = Jilles|volume=60|issue = 3|pages=1277–1305|doi-access = free}}</ref>
最短的这样的程序意味着未压缩的存储变量更有可能导致计算变量。
===Noise models===
噪音模型
Incorporate an independent noise term in the model to compare the evidences of the two directions.
在模型中引入一个独立的噪声项，比较两个方向的证据。
Here are some of the noise models for the hypothesis Y → X with the noise E:
下面是一些假设 y → x 有噪声 e 的噪声模型:
* Additive noise:<ref>Hoyer, Patrik O., et al. "[https://papers.nips.cc/paper/3548-nonlinear-causal-discovery-with-additive-noise-models.pdf Nonlinear causal discovery with additive noise models]." NIPS. Vol. 21. 2008.</ref> <math>Y = F(X)+E</math>
* Linear noise:<ref>{{cite journal | last1 = Shimizu | first1 = Shohei | display-authors = etal | year = 2011 | title = DirectLiNGAM: A direct method for learning a linear non-Gaussian structural equation model | url = http://www.jmlr.org/papers/volume12/shimizu11a/shimizu11a.pdf | journal = The Journal of Machine Learning Research | volume = 12 | issue = | pages = 1225–1248 }}</ref> <math>Y = pX + qE</math>
* Post-non-linear:<ref>Zhang, Kun, and Aapo Hyvärinen. "[https://arxiv.org/pdf/1205.2599 On the identifiability of the post-nonlinear causal model]." Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence. AUAI Press, 2009.</ref> <math>Y = G(F(X)+E)</math>
* Heteroskedastic noise: <math>Y = F(X)+E.G(X)</math>
* Functional noise:<ref name="Mooij">Mooij, Joris M., et al. "[http://papers.nips.cc/paper/4173-probabilistic-latent-variable-models-for-distinguishing-between-cause-and-effect.pdf Probabilistic latent variable models for distinguishing between cause and effect]." NIPS. 2010.</ref> <math>Y = F(X,E)</math>
The common assumption in these models are:
这些模型的共同假设是:
* There are no other causes of Y.
* X and E have no common causes.
* Distribution of cause is independent from causal mechanisms.
On an intuitive level, the idea is that the factorization of the joint distribution P(Cause, Effect) into P(Cause)*P(Effect | Cause) typically yields models of lower total complexity than the factorization into P(Effect)*P(Cause | Effect). Although the notion of “complexity” is intuitively appealing, it is not obvious how it should be precisely defined.<ref name="Mooij"/> A different family of methods attempt to discover causal "footprints" from large amounts of labeled data, and allow the prediction of more flexible causal relations.<ref>Lopez-Paz, David, et al. "[http://www.jmlr.org/proceedings/papers/v37/lopez-paz15.pdf Towards a learning theory of cause-effect inference]" ICML. 2015</ref>
在直观的层面上，这个想法是联合分布 p (因果)到 p (因果) * p (效果 | 原因)的因式分解通常产生的模型的总复杂性低于因式分解到 p (效果) * p (因果)。尽管“复杂性”的概念在直觉上很吸引人，但是它应该如何精确定义却并不明显。
== In statistics and economics ==
在统计学和经济学领域
{{Main|Causality#Statistics and economics}}
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。
== In social science ==
在社会科学领域
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。
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。
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。
== See also ==
参见
* [[Causal analysis]]
* [[Granger causality]]
* [[Multivariate statistics]]
* [[Partial least squares regression]]
* [[Pathogenesis]]
* [[Pathology]]
* [[Regression analysis]]
* [[Transfer entropy]]
== References ==
参考资料
{{Reflist}}
==Bibliography==
参考书目
* {{cite book |ref=what_if |last1=Hernán |first1=MA |author-link1=Miguel Hernán |last2=Robins |first2=JM |author-link2=James Robins |title=Causal Inference: What If |location=Barnsley |publisher=Boca Raton: Chapman & Hall/CRC |date=2020-01-21|url=https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/}}
==External links==
外部链接
{{Commons category}}
*[http://clopinet.com/isabelle/Projects/NIPS2013/ NIPS 2013 Workshop on Causality]
*[http://webdav.tuebingen.mpg.de/causality/ Causal inference at the Max-Planck-Institute for Intelligent Systems Tübingen]
{{Portal bar|Science}}
{{Use dmy dates|date=October 2019}}
[[Category:Causal inference| ]]
[[Category:Graphical models]]
类别: 图形模型
[[Category:Regression analysis]]
类别: 回归分析
[[Category:Inductive reasoning]]
类别: 归纳推理
[[Category:Philosophy of statistics]]
分类: 统计哲学
<noinclude>
<small>This page was moved from [[wikipedia:en:Causal inference]]. Its edit history can be viewed at [[因果推断/edithistory]]</small></noinclude><noinclude>
<small>This page was moved from [[mywiki:zh-cn:因果推断]]. Its edit history can be viewed at [[因果推断/edithistory]]</small></noinclude>
{{Expert needed|date=October 2019}}
'''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]].
因果推理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病因学。因果推理就是因果推理推理的一个例子。
==Definition==
定义
Inferring the [[cause]] of something has been described as:
推断某事的起因被描述为:
*"...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>
==Methods==
方法
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流行病学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。这个推论统计学有助于判断数据是由偶然性引起的，也称为随机变异，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。
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结构方程模型和虚拟事实模型。
==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.
流行病学研究特定生物群体的健康和疾病模式，以推断原因和结果。暴露于一个推定的危险因素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，科赫的假设从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年描述的布拉德福德·希尔准则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。
In [[molecular epidemiology]] the phenomena studied are on a [[molecular biology]] level, including genetics, where [[biomarkers]] are evidence of cause or effects.
在21分子流行病学，研究的现象是在分子生物学水平上，包括遗传学，其中生物标志物是原因或影响的证据。
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}}
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分子病理学的影响的证据。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异质性，独特的疾病原理，疾病表型分型和亚型是生物医学和公共卫生科学的趋势，例如个体化医学和精准医学。
==In computer science==
在计算机科学领域
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。
===Algorithmic information models===
算法信息模型
Compare two programs, both of which output both X and Y.
比较两个同时输出 x 和 y 的程序。
* Store Y and a compressed form of X in terms of uncompressed Y.
* Store X and a compressed form of Y in terms of uncompressed X.
The shortest such program implies the uncompressed stored variable more-likely causes the computed variable.<ref>Kailash Budhathoki and Jilles Vreeken "[http://eda.mmci.uni-saarland.de/pubs/2016/origo-budhathoki,vreeken.pdf Causal Inference by Compression]" 2016 IEEE 16th International Conference on Data Mining (ICDM)</ref><ref>{{Cite journal |doi = 10.1007/s10115-018-1286-7|title = Telling cause from effect by local and global regression|journal = Knowledge and Information Systems|year = 2018|last1 = Marx|first1 = Alexander|last2 = Vreeken|first2 = Jilles|volume=60|issue = 3|pages=1277–1305|doi-access = free}}</ref>
最短的这样的程序意味着未压缩的存储变量更有可能导致计算变量。
===Noise models===
噪音模型
Incorporate an independent noise term in the model to compare the evidences of the two directions.
在模型中引入一个独立的噪声项，比较两个方向的证据。
Here are some of the noise models for the hypothesis Y → X with the noise E:
下面是一些假设 y → x 有噪声 e 的噪声模型:
* Additive noise:<ref>Hoyer, Patrik O., et al. "[https://papers.nips.cc/paper/3548-nonlinear-causal-discovery-with-additive-noise-models.pdf Nonlinear causal discovery with additive noise models]." NIPS. Vol. 21. 2008.</ref> <math>Y = F(X)+E</math>
* Linear noise:<ref>{{cite journal | last1 = Shimizu | first1 = Shohei | display-authors = etal | year = 2011 | title = DirectLiNGAM: A direct method for learning a linear non-Gaussian structural equation model | url = http://www.jmlr.org/papers/volume12/shimizu11a/shimizu11a.pdf | journal = The Journal of Machine Learning Research | volume = 12 | issue = | pages = 1225–1248 }}</ref> <math>Y = pX + qE</math>
* Post-non-linear:<ref>Zhang, Kun, and Aapo Hyvärinen. "[https://arxiv.org/pdf/1205.2599 On the identifiability of the post-nonlinear causal model]." Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence. AUAI Press, 2009.</ref> <math>Y = G(F(X)+E)</math>
* Heteroskedastic noise: <math>Y = F(X)+E.G(X)</math>
* Functional noise:<ref name="Mooij">Mooij, Joris M., et al. "[http://papers.nips.cc/paper/4173-probabilistic-latent-variable-models-for-distinguishing-between-cause-and-effect.pdf Probabilistic latent variable models for distinguishing between cause and effect]." NIPS. 2010.</ref> <math>Y = F(X,E)</math>
The common assumption in these models are:
这些模型的共同假设是:
* There are no other causes of Y.
* X and E have no common causes.
* Distribution of cause is independent from causal mechanisms.
On an intuitive level, the idea is that the factorization of the joint distribution P(Cause, Effect) into P(Cause)*P(Effect | Cause) typically yields models of lower total complexity than the factorization into P(Effect)*P(Cause | Effect). Although the notion of “complexity” is intuitively appealing, it is not obvious how it should be precisely defined.<ref name="Mooij"/> A different family of methods attempt to discover causal "footprints" from large amounts of labeled data, and allow the prediction of more flexible causal relations.<ref>Lopez-Paz, David, et al. "[http://www.jmlr.org/proceedings/papers/v37/lopez-paz15.pdf Towards a learning theory of cause-effect inference]" ICML. 2015</ref>
在直观的层面上，这个想法是联合分布 p (因果)到 p (因果) * p (效果 | 原因)的因式分解通常产生的模型的总复杂性低于因式分解到 p (效果) * p (因果)。尽管“复杂性”的概念在直觉上很吸引人，但是它应该如何精确定义却并不明显。
== In statistics and economics ==
在统计学和经济学领域
{{Main|Causality#Statistics and economics}}
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。
== In social science ==
在社会科学领域
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。
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。
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。
== See also ==
参见
* [[Causal analysis]]
* [[Granger causality]]
* [[Multivariate statistics]]
* [[Partial least squares regression]]
* [[Pathogenesis]]
* [[Pathology]]
* [[Regression analysis]]
* [[Transfer entropy]]
== References ==
参考资料
{{Reflist}}
==Bibliography==
参考书目
* {{cite book |ref=what_if |last1=Hernán |first1=MA |author-link1=Miguel Hernán |last2=Robins |first2=JM |author-link2=James Robins |title=Causal Inference: What If |location=Barnsley |publisher=Boca Raton: Chapman & Hall/CRC |date=2020-01-21|url=https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/}}
==External links==
外部链接
{{Commons category}}
*[http://clopinet.com/isabelle/Projects/NIPS2013/ NIPS 2013 Workshop on Causality]
*[http://webdav.tuebingen.mpg.de/causality/ Causal inference at the Max-Planck-Institute for Intelligent Systems Tübingen]
{{Portal bar|Science}}
{{Use dmy dates|date=October 2019}}
[[Category:Causal inference| ]]
[[Category:Graphical models]]
类别: 图形模型
[[Category:Regression analysis]]
类别: 回归分析
[[Category:Inductive reasoning]]
类别: 归纳推理
[[Category:Philosophy of statistics]]
分类: 统计哲学
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