{"id":365,"date":"2022-09-29T17:28:45","date_gmt":"2022-09-29T14:28:45","guid":{"rendered":"https:\/\/quecst.qcri.org\/blog\/?p=365"},"modified":"2022-10-26T13:28:46","modified_gmt":"2022-10-26T10:28:46","slug":"when-mere-correlations-are-not-enough-the-granger-causality-test","status":"publish","type":"post","link":"https:\/\/acua.qcri.org\/blog\/when-mere-correlations-are-not-enough-the-granger-causality-test\/","title":{"rendered":"When mere correlations are not enough: The Granger Causality test"},"content":{"rendered":"

In most data science-related problems, datasets consist of multiple variables, in which independent variables might depend on other independent variables.<\/p>\n

When the variables in datasets represent observations at different times, we call this dataset a time series set.<\/p>\n

The time interval in these data sets may be hourly, daily, weekly, monthly, quarterly, annually, etc.<\/p>\n

One way to quantify these multivariate relationships in datasets is linear regression. A regression model might indicate a strong relationship between two or more variables. Still, these variables may be unrelated in reality. In this case, predictions based on these relationships fail due to a lack of domain knowledge [1]. For instance, researchers might build multilinear regression models without knowing the nature of the relationship between variables. Suppose such regression models produce a high R square value. In that case, the resulting model might further mislead the interpretations and generate poor predictions or forecasting [1].<\/p>\n

Consider the following time-series graph: variable X has a direct influence on variable Y, but there is a lag (i.e., time difference) of 5 between X and Y, so we cannot use the correlation matrix [2].<\/p>\n

\"When
When mere correlations are not enough: The Granger Causality test<\/figcaption><\/figure>\n

For example, consider X an increase in positive covid-19 cases in a city and Y an increase in the number of people hospitalized. For better forecasting, we would like to know if there is a causal relationship between the variables X and Y [1].<\/p>\n

To solve this issue, Prof. Clive W.J. Granger, recipient of the 2003 Nobel Prize in Economics, developed the causality concept to improve forecasting performance [4].<\/p>\n

It is, basically, an econometric hypothetical test for verifying the usage of one variable in forecasting another in multivariate time series data with a particular lag.<\/p>\n

The requirements for performing the Granger Causality test include the following:<\/p>\n