Under an additive outcome mechanism, the joint interventional effect equals the sum of single-intervention effects minus (K-1) times the observational expectation, so joint interventions are identifiable from single-variable interventions and observational data.
Deep Learning based Forecasting: a case study from the online fashion industry
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abstract
Demand forecasting in the online fashion industry is particularly amendable to global, data-driven forecasting models because of the industry's set of particular challenges. These include the volume of data, the irregularity, the high amount of turn-over in the catalog and the fixed inventory assumption. While standard deep learning forecasting approaches cater for many of these, the fixed inventory assumption requires a special treatment via controlling the relationship between price and demand closely. In this case study, we describe the data and our modelling approach for this forecasting problem in detail and present empirical results that highlight the effectiveness of our approach.
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Learning Joint Interventional Effects from Single-Variable Interventions in Additive Models
Under an additive outcome mechanism, the joint interventional effect equals the sum of single-intervention effects minus (K-1) times the observational expectation, so joint interventions are identifiable from single-variable interventions and observational data.