Reverse Causation
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A Reverse Causation is a causal relationship between two variables which not certain and can be reversed.
- AKA: Reverse Causality.
- Context:
- It can be defined as: if A causes B is a direct causation then B causes A is a reverse causation.
- See: Endogenous Variable, Exogenous Variable, Causal Inference, Instrumental Variable, Conditional, Correlation.
References
2016
- (Wikipedia, 2016) ⇒ http://en.wikipedia.org/wiki/https://www.wikiwand.com/en/Instrumental_variable
- In statistics, econometrics, epidemiology and related disciplines, the method of instrumental variables (IV) is used to estimate causal relationships when controlled experiments are not feasible or when a treatment is not successfully delivered to every unit in a randomized experiment.[1] Intuitively, IV is used when the correlation between the explanatory variable and the dependent variable does not plausibly reflect the causal relationship between the two. A valid instrument induces changes in the explanatory variable but has no independent effect on the dependent variable, allowing a researcher to uncover the causal effect of the explanatory variable on the dependent variable.
- Instrumental variable methods allow for consistent estimation when the explanatory variables (covariates) are correlated with the error terms in a regression model. Such correlation may occur when changes in the dependent variable change the value of at least one of the covariates ("reverse" causation), when there are omitted variables that affect both the dependent and independent variables, or when the covariates are subject to measurement error. Explanatory variables which suffer from one or more of these issues in the context of a regression are sometimes referred to as endogenous. In this situation, ordinary least squares produces biased and inconsistent estimates.[2] However, if an instrument is available, consistent estimates may still be obtained. An instrument is a variable that does not itself belong in the explanatory equation but is correlated with the endogenous explanatory variables, conditional on the value of other covariates.
- (Wikipedia, 2016) ⇒ http://en.wikipedia.org/wiki/Correlation_does_not_imply_causation#Causality_predicted_by_an_extrapolation_of_trends
- (...) Then experimental studies are impossible and only pre-existing data are available, as is usually the case for example in economics, regression analysis can be used. Factors other than the potential causative variable of interest are controlled for by including them as regressors in addition to the regressor representing the variable of interest. False inferences of causation due to reverse causation (or wrong estimates of the magnitude of causation due the presence of bidirectional causation) can be avoided by using explanators (regressors) that are necessarily exogenous, such as physical explanators like rainfall amount (as a determinant of, say, futures prices), lagged variables whose values were determined before the dependent variable's value was determined, instrumental variables for the explanators (chosen based on their known exogeneity), etc. See Causality#Statistics and economics. Spurious correlation due to mutual influence from a third, common, causative variable, is harder to avoid: the model must be specified such that there is a theoretical reason to believe that no such underlying causative variable has been omitted from the model. In particular, underlying time trends of both the dependent variable and the independent (potentially causative) variable must be controlled for by including time as another independent variable.
- ↑ Imbens, G.; Angrist, J. (1994). "Identification and estimation of local average treatment effects". Econometrica 62 (2): 467–476. JSTOR 2951620.
- ↑ Bullock, J. G.; Green, D. P.; Ha, S. E. (2010). "Yes, But What’s the Mechanism? (Don’t Expect an Easy Answer)". Journal of Personality and Social Psychology 98 (4): 550–558. doi:10.1037/a0018933.