Hello Researchers,
I wonder if one can use Granger's Causality Test on Non-Time Series data, for example I want to study relation between happiness in general and happiness at work and want to know who causes other . I collected data from random working people using 2 scales measuring 2 constructs. Can I use Granger's test here?
Thanks
Yes. One example would be instrumental variables (this example is from Wikipedia):
Informally, in attempting to estimate the causal effect of some variable x on another y, an instrument is a third variable z which affects y only through its effect on x. For example, suppose a researcher wishes to estimate the causal effect of smoking on general health. Correlation between health and smoking does not imply that smoking causes poor health because other variables may affect both health and smoking, or because health may affect smoking. It is at best difficult and expensive to conduct controlled experiments on smoking status in the general population. The researcher may attempt to estimate the causal effect of smoking on health from observational data by using the tax rate for tobacco products as an instrument for smoking. The tax rate for tobacco products is a reasonable choice for an instrument because the researcher assumes that it can only be correlated with health through its effect on smoking. If the researcher then finds tobacco taxes and state of health to be correlated, this may be viewed as evidence that smoking causes changes in health.
Another example would be some natural experiment. For example: There was some event that you know can impact only the happiness in work. In this case, if you observe change in happiness in general, it must be because happiness in work influenced happiness in general.
Is there any way to test causality in such cases? If I run a a linear regression both are acting as significant predictor of each other
Yes. One example would be instrumental variables (this example is from Wikipedia):
Informally, in attempting to estimate the causal effect of some variable x on another y, an instrument is a third variable z which affects y only through its effect on x. For example, suppose a researcher wishes to estimate the causal effect of smoking on general health. Correlation between health and smoking does not imply that smoking causes poor health because other variables may affect both health and smoking, or because health may affect smoking. It is at best difficult and expensive to conduct controlled experiments on smoking status in the general population. The researcher may attempt to estimate the causal effect of smoking on health from observational data by using the tax rate for tobacco products as an instrument for smoking. The tax rate for tobacco products is a reasonable choice for an instrument because the researcher assumes that it can only be correlated with health through its effect on smoking. If the researcher then finds tobacco taxes and state of health to be correlated, this may be viewed as evidence that smoking causes changes in health.
Another example would be some natural experiment. For example: There was some event that you know can impact only the happiness in work. In this case, if you observe change in happiness in general, it must be because happiness in work influenced happiness in general.
Yes, I also agree with Dr. Peter Molnár. Thank you very much for your explanation sir.
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