What kind of forecasting method is based on the assumption of a cause-effect relationship with other variables?

The correct choice is based on the understanding that causal forecasting methods explicitly seek to identify relationships between a dependent variable and one or more independent variables. This approach relies on the assumption that certain factors or predictors can directly influence the outcome being forecasted. For instance, if a business wants to forecast sales, it might consider variables such as marketing expenditure, economic indicators, or product prices, establishing a cause-effect relationship.

In contrast, time series forecasting focuses on historical data points over specific intervals without considering external causal factors. Qualitative forecasting relies more on expert judgment and intuition rather than on quantitative relationships, while exponential smoothing is a technique in time series forecasting that gives different weights to past observations but does not involve the identification of causal factors. Causal methods, therefore, stand out because they specifically address how other variables impact the variable of interest, making them suitable for analyses where external influences are significant.