An improved method and system for forecasting product demand using a causal methodology, based on multiple regression techniques. The causal method uses both historical and future values of causal factors for causal forecasting. Historical values are used to build a causal model, i.e., to determine the influence of the causal factors upon the demand for a product, and future values are used to generate demand uplifts which applied to an initial demand forecast based upon historical product demand. The improved causal method provides different processes for the calculation of demand uplifts associated with seasonal variables, such as temperature, than typical, non-seasonal causal variables, such as product price. Demand uplifts for seasonal variables are determined from the difference between a forecast value for the seasonal variable and an average of corresponding historical, prior-year, values of the seasonal variable, and demand uplifts for non-seasonal variables are determined from the difference between a forecast value for the non-seasonal variable and an average of recent values of the non-seasonal variable.