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Demand shaping in practice - application of causal inference models for an e-commerce platform
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Orientador(es)
Resumo(s)
VOIDS 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.
Descrição
Palavras-chave
Demand Shaping Causal Inference Double Machine Learning Granger Causality Linear Regression