Abstract:

Price elasticity is a metric commonly used by pricing managers to optimize pricing strategies. Traditionally, historical observational data has been used to estimate price elasticity through classical econometric models, such as linear regression and log-log models. However, these methods consider only associations, treating historical data as patterns of observation. For many applications, merely learning from past patterns is insufficient; there is a need to develop methodologies that quantify the impact of interventions. In this study, we introduce a causal inference framework as a solution to this challenge. We analyze the types of interactions present in causal systems and explore their manifestations in the context of price management. Additionally, we illustrate how conflating association with intervention can adversely affect profits.