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{{Main|Causality#Statistics and economics}}

{{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>

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真实的因果关系和虚假的相关性。第一，经济学家根据经济理论('''<font color='#ff8000>理论驱动theory-driven</font>'''的计量经济学)构建回归模型，从而确定因果关系的方向。

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真实的因果关系和虚假的相关性。第一，经济学家根据经济理论('''<font color='#ff8000>理论驱动theory-driven</font>'''的计量经济学)构建回归模型，从而确定因果关系的方向。

例如，如果研究降雨与未来商品价格之间的依赖关系，那么一个广义上建构的理论表明，降雨可以影响价格，但未来价格不能改变降雨量。<ref>{{Cite book|last=Simon|first=Herbert|title=Models of Discovery|publisher=Springer|year=1977|location=Dordrecht|page=52}}</ref> . 第二，'''<font color = '#ff8000'>工具变量instrumental variables(IV)</font>'''技术可以通过引入其他已知不受因变量的影响的工具变量来消除任何反向因果关系。第三，经济学家会考虑时间优先级来选择合适的具体模型。由于'''<font color='#ff8000>偏相关partial correlations</font>'''是对称的，人们不能在相关的基础上确定因果关系的方向。经济学家基于因果关系的'''<font color='#ff8000>概率观点probabilistic view</font>'''假设，原因必须在时间上优先于其结果。这导致经济学家使用较早发生的现象作为自变量，并开发适用于时间序列分析的因果关系检验计量经济方法（例如，'''<font color = '#ff8000'>格兰杰因果检验</font>'''）<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>。第五，有些做法包括了其他回归项，从而确保'''<font color = '#ff8000'>混淆变量confounding variables</font>'''不会导致某个回归因素显得明显虚假，但遭受'''<font color = '#ff8000'>多重共线性multicollinearity</font>'''问题困扰的领域，如宏观经济学，原则上不可能包括所有混淆因素，因此计量经济模型容易出现'''<font color = '#ff8000'>共因谬误common-cause fallacy</font>'''<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>。近年来，'''<font color = '#ff8000'>以研究设计为基础的计量经济学design-based econometrics</font>'''运动已经推广使用自然实验和准实验研究设计来解决'''<font color = '#ff8000'>虚假相关spurious correlations</font>'''的问题<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>。

例如，如果研究降雨与未来商品价格之间的依赖关系，那么一个广义上建构的理论表明，降雨可以影响价格，但未来价格不能改变降雨量。<ref>{{Cite book|last=Simon|first=Herbert|title=Models of Discovery|publisher=Springer|year=1977|location=Dordrecht|page=52}}</ref> . 第二，'''<font color = '#ff8000'>工具变量instrumental variables(IV)</font>'''技术可以通过引入其他已知不受因变量的影响的工具变量来消除任何反向因果关系。【第三，经济学家会考虑时间优先级来选择合适的具体模型。由于'''<font color='#ff8000>偏相关partial correlations</font>'''是对称的，人们不能在相关的基础上确定因果关系的方向。经济学家基于因果关系的'''<font color='#ff8000>概率观点probabilistic view</font>'''假设，原因必须在时间上优先于其结果。这导致经济学家使用较早发生的现象作为自变量，并开发适用于时间序列分析的因果关系检验计量经济方法（例如，'''<font color = '#ff8000'>格兰杰因果检验</font>'''）<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>。第五，有些做法包括了其他回归项，从而确保'''<font color = '#ff8000'>混淆变量confounding variables</font>'''不会导致某个回归因素显得明显虚假，但遭受'''<font color = '#ff8000'>多重共线性multicollinearity</font>'''问题困扰的领域，如宏观经济学，原则上不可能包括所有混淆因素，因此计量经济模型容易出现'''<font color = '#ff8000'>共因谬误common-cause fallacy</font>'''<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>。近年来，'''<font color = '#ff8000'>以研究设计为基础的计量经济学design-based econometrics</font>'''运动已经推广使用自然实验和准实验研究设计来解决'''<font color = '#ff8000'>虚假相关spurious correlations</font>'''的问题<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>。】

 

Third, the principle that effects cannot precede causes can be invoked, by including on the right side of the regression only variables that precede in time the dependent variable. Fourth, other regressors are included to ensure that confounding variables are not causing a regressor to spuriously appear to be significant. Correlation by coincidence, as opposed to correlation reflecting actual dependence of the underlying process, can be ruled out by using large samples and by performing cross validation to check that correlations are maintained on data that were not used in the regression.

第四，有些方法包括了其他回归因素，以确保'''<font color = '#ff8000'>混淆变量confounding variables</font>'''不会导致一个回归项虚假地呈现显著。通过使用大样本和交叉验证检查在回归中未使用的数据上是否保持了相关性，可以排除巧合的，而不是反映实际依赖内在过程的相关性。】

== In social science ==

== In social science ==