50% Boost in Accuracy: The Power of Causal Inference in Demand Forecasting

A junior data scientist has identified a potential 50% increase in accuracy for their team’s demand forecasting model through the application of causal inference. The current model, while functional, falls short in performance. After dedicating a day to researching causal inference, the junior data scientist believes this approach could significantly enhance their model’s effectiveness. Now, the challenge lies in presenting this idea to the principal data scientist for approval. The junior data scientist is considering whether to develop a proof of concept independently or to seek guidance on the best approach to move forward. This situation highlights the critical role that statistical methods like causal inference can play in improving predictive models in data science.

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