GCF: Estimating Unobserved Demand Using Graph Causal Forecasting

Abstract

Time series data, prevalent in fields like medical, e-commerce, finance, etc., is used for forecasting, such as predicting next quarter’s product demand based on past trends. However, some problems necessitate causal models to answer questions like “What the product demand would have been without a specific intervention (e.g., products with slower delivery time suppressed from the search results)?” Such questions require causal models to estimate unobserved counterfactual outcome. In this paper, we propose a novel Graph Causal Forecasting (GCF) model, that predicts the unobserved demand leveraging the relationship of a product with other similar products in the marketplace (spatial aspect), along with change in demand over time for each product (temporal aspect). The core idea is to estimate the counterfactual outcome using a synthetic control unaffected by the treatment. Our approach uses RGCN-dilated CNN based network, which leverages domain knowledge to automatically design a synthetic control during training. Using GCF for our demand forecasting problem, we achieve 75.3% lower MAPE compared to baseline. We use the forecasted values to recommend high demand products, in terms of our business metric (discussed later) which tracks the quality of these recommendations, we achieve a significant jump of 61.2%. Moreover, it adds 67.8% more high demand products to the marketplace, compared to existing model in production. Deployment of GCF in 2023, led to +1399 bps improvement in number of products with a view from customers, and +310 bps improvement in number of products with a sale. We also compare GCF with state of the art forecasting methods on a semi-synthetic data, created by simulating a treatment on open source traffic data METR-LA. We achieve 30% lower MSE against TGCN, a time series forecasting approach and 30% lower MSE against CRN and 25% lower MSE against Google Causal Impact model, both of which are causal forecasting approaches.