Mixed causal structure discovery with application to prescriptive pricing

Abstract

Prescriptive pricing is one of the most advanced pricing techniques, which derives the optimal price strategy to maximize the future profit/revenue by carrying out a two-stage process, demand modeling and price optimization. Demand modeling tries to reveal price-demand laws by discovering causal relationships among demands, prices, and objective factors, which is the foundation of price optimization. Existing methods either use regression or causal learning for uncovering the price-demand relations, but suffer from pain points in either accuracy/efficiency or mixed data type processing, while all of these are actual requirements in practical pricing scenarios. This paper proposes a novel demand modeling technique for practical usage. Speaking concretely, we propose a new locally consistent information criterion named MIC, and derive MIC-based inference algorithms for an accurate recovery of causal structure on mixed factor space. Experiments on simulate/real datasets show the superiority of our new approach in both price-demand law recovery and demand forecasting, as well as show promising performance to support optimal pricing.