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社会科学越来越倾向评估因果关系的定量框架。框架中的很大一部分已经被描述为一种提供更严格的'''<font color = '#ff8000'>社会科学方法social science methodology</font>'''的方式。1994年，'''<font color = '#ff8000'>加里·金Gary King</font>'''、'''<font color = '#ff8000'>罗伯特 · 基奥汉Robert Keohane</font>'''和'''<font color = '#ff8000'>西德尼 · 维尔巴Sidney Verba</font>'''合著的《'''<font color = '#ff8000'>设计社会学问卷Designing Social Inquiry</font>'''》对政治科学产生了重大影响。'''<font color = '#32cd32'>金、基奥汉，和维尔巴（通常缩写为 KKV）建议研究人员同时采用定量和定性的方法，采用统计推论的语言来更清楚地说明他们感兴趣的主题和分析的单位。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.</font>'''<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>定量方法的支持者也越来越多地采用'''<font color = '#ff8000'>唐纳德 · 鲁宾Donald Rubin</font>'''开发的'''<font color = '#ff8000'>潜在结果框架potential outcomes framework</font>'''作为推断因果关系的标准。

社会科学越来越倾向评估因果关系的定量框架。框架中的很大一部分已经被描述为一种提供更严格的'''<font color = '#ff8000'>社会科学方法social science methodology</font>'''的方式。1994年，'''<font color = '#ff8000'>加里·金Gary King</font>'''、'''<font color = '#ff8000'>罗伯特 · 基奥汉Robert Keohane</font>'''和'''<font color = '#ff8000'>西德尼 · 维尔巴Sidney Verba</font>'''合著的《'''<font color = '#ff8000'>设计社会学问卷Designing Social Inquiry</font>'''》对政治科学产生了重大影响。'''<font color = '#32cd32'>金、基奥汉，和维尔巴（通常缩写为 KKV）建议研究人员同时采用定量和定性的方法，采用统计推论的语言来更清楚地说明他们感兴趣的主题和分析的单位。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.</font>'''<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>定量方法的支持者也越来越多地采用'''<font color = '#ff8000'>唐纳德 · 鲁宾Donald Rubin</font>'''开发的'''<font color = '#ff8000'>潜在结果框架potential outcomes framework</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 '''<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。

合适地应用定量方法来推断因果关系的相关争论导致了对研究'''<font color = '#ff8000'>可重复性reproducibility</font>'''的更多关注。对广泛被使用的方法持批评态度的人认为，研究人员利用'''<font color = '#ff8000'>数据挖掘Data dredging</font>'''或'''<font color = '#ff8000'>p-hacking</font>'''技术以在虚假相关的基础上发表文章<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>。为了避免这种情况的发生，一些人主张研究人员在进行研究之前'''<font color = '#ff8000'>预注册preregister</font>'''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<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>。社会科学内部关于方法论和可重复性的争论有时是尖锐的{{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}}

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。

社会学家 Herbert Smith 和政治学家 James Mahoney 、 Gary Goertz 引用了统计学家 Paul Holland 的观察结果，Paul Holland在1986年发表了一篇名为《'''<font color = '#ff8000'>统计学和因果推断Statistics and Causal Inference</font>'''》的文章，认为推论统计学最适合于评估“结果的原因”而不是“结果的原因”。定性方法学家认为，形式化的因果关系模型，包括'''<font color = '#ff8000'>过程追踪process tracing</font>'''和'''<font color = '#ff8000'>模糊集理论fuzzy set theory</font>'''，通过在某个案例研究内识别关键因素或在几个案例研究之间比较的过程提供了推断因果关系的机会。这些方法对于那些可能的观察数量有限或存在混淆变量从而限制统计推论适用性的课题也是有价值的。

== See also ==

== See also ==