Counterfactual Inference for Consumer Choice Across Many Product Categories

Understand

This paper proposes a method for estimating consumer preferences among discrete choices, where the consumer chooses at most one product in a category, but selects from multiple categories in parallel.

• The consumer's utility is additive in the different categories.
• Her preferences about product attributes as well as her price sensitivity vary across products and are in general correlated across products.
• We build on techniques from the machine learning literature on probabilistic models of matrix factorization, extending the methods to account for time-varying product attributes and products going out of stock.