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{{Main|Causality#Statistics and economics}}

{{Main|Causality#Statistics and economics}}

   --[[用户:嘉树|嘉树]]（[[用户讨论:嘉树|讨论]]） 本段似有缺漏，而英文词条和本段内容又有一些不同。因此，（1）先写本段的翻译，（2）再写英文词条中有不同的地方，（3）最后整合一下。【】括起来的是在两个版本中需要照应和修改的部分。

   --[[用户:嘉树|嘉树]]（[[用户讨论:嘉树|讨论]]） 本段似有错漏，英文词条和本段内容有一些不同。因此，（1）先写本段的翻译，（2）再写英文词条中有不同的地方，（3）最后整合一下。【】括起来的是在两个版本中需要照应和修改的部分。

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>