Causal forecasting is a method used to predict future events by examining the cause-and-effect relationships among variables. It goes beyond simple trend analysis and considers various factors that could influence the forecasted quantity.
Regression analysis is a statistical process for estimating the relationships among variables. In the context of causal forecasting, regression is used to identify and measure the impact of one or more independent variables on a dependent variable. This technique is particularly useful when you want to forecast a variable based on the relationship it has with other variables.
For example, a company might use regression analysis to forecast sales based on advertising spend, assuming that there is a causal relationship between advertising and sales. The regression model would allow the company to quantify the expected increase in sales for each unit of increased advertising spend.
References
The information provided here is based on the general principles of causal forecasting and regression analysis, which are well-established in the field of supply management and statistics

Section: Supply Chain Strategy, Design, and Compliance