A DETERMINISTIC FORECASTING FRAMEWORK WITH CAUSAL SCENARIO ANALYSIS

This paper presents a novel deterministic forecasting model for pre-dicting future business revenue over a certain horizon by incorporating causal "what-if" scenario analysis.Using monthly historical data on various business metrics, the framework builds a flexible, component-based forecast indepen-dent of any specific revenue model (e.g., applicable to subscription or ad-based businesses alike).The deterministic model captures causal relationships between business growth, goals, and revenue generation, allowing explicit simulation of interventions, such as feature launches, that boost business growth by a specified percentage.We detail the forecasting framework, causal modeling methodology, deterministic assumptions, and mathematical formulation of the model.An il-lustrative use cases demonstrate how different intervention timings and strengths produce adjusted revenue forecasts.