Han, QiweiMiotto, Greta2024-09-272023-06-072023-05-17http://hdl.handle.net/10362/172515VOIDS provides deep learning-based demand forecasting. To provide their customers with countermeasures in response to different supply/demand scenarios, VOIDS needs to infer the causal relationship of their clients’ data. This thesis seeks to investigate whether traditional econometric models as well as newer machine learning models can be used to provide VOIDS with a scalable solution for doing causal inference for their clients. The joint part will be focused on theoretical discussions and testing, while individual part 1 compares the results with a prediction model.engDemand shapingForecastingCausal inferencePredictive modelsCausalitymaster thesis203516303