Han, QiweiSolbakken, Claus Åne Sørbøe2025-05-092023-06-072023-05-17http://hdl.handle.net/10362/182913VOIDS 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 thesis is split into two parts, with part one focused on theoretical discussions and testing, while part 2 presents a practical application of the results for VOIDS’ platform.engDemandShapingCausalInferenceDoubleMachineLearningGrangerCausalityLinearRegressionmaster thesis203366603