AI engineering glossary · Time series recsys
What is granger causality?
Granger causality: A statistical test of whether past values of one series improve the prediction of another beyond what its own past gives. It shows predictive usefulness, not true cause and effect.
Granger causality: an example
Past ad spend improving forecasts of weekly sales after using past sales is evidence that ad spend Granger-causes sales.
Related terms
More Time series recsys terms
- GRU4Rec
- Hierarchical forecasting
- Holiday effect
- Holt's linear method
- Holt-Winters method
- Hybrid recommender
- Intermittent demand
- Inverse propensity scoring
Where you learn it
Granger causality is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.