Counterfactual inference for consumer choice across many product categories

By Rob Donnelly, Francisco J. R. Ruiz, David M. Blei, Susan Athey. 2019

40 citations recorded by the source corpus.

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 may be 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. We evaluate the performance of the model using held-out data from weeks with price changes or out of stock products. We show that our model improves over traditional modeling approaches that consider each category in isolation. One source of the improvement is the ability of the model to accurately estimate heterogeneity in preferences (by pooling information across categories); another source of improvement is its ability to estimate the preferences of consumers who have rarely or never made a purchase in a given category in the training data. Using held-out data, we show that our model can accurately distinguish which consumers are most price sensitive to a given product. We consider counterfactuals such as personally targeted price discounts, showing that using a richer model such as the one we propose substantially increases the benefits of personalization in discounts.

Subfields: Economics and Econometrics, General Decision Sciences, Marketing, Causal Inference & Discovery, Economics & Operations Research

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

  1. Francisco J. R. Ruiz
  2. David M. Blei
  3. Susan Athey
  4. David McAdams
  5. Philip J. Reny
  6. Jakub Kastl
  7. Arnaud Costinot
  8. Philippe Jehiel

In General Decision Sciences

  1. David M. Blei
  2. Susan Athey

In Marketing

  1. Susan Athey
  2. Francisco J. R. Ruiz
  3. David M. Blei
  4. Guido W. Imbens
  5. Arnaud Costinot
  6. Angelo Mele

In Causal Inference & Discovery

  1. David M. Blei
  2. Francisco J. R. Ruiz
  3. Susan Athey
  4. Stefan Wager
  5. Mihaela van der Schaar
  6. Ahmed M. Alaa
  7. Guido W. Imbens
  8. Victor Chernozhukov

In Economics & Operations Research

  1. Susan Athey
  2. Francisco J. R. Ruiz
  3. David M. Blei
  4. Guido W. Imbens
  5. Stefan Wager

Citations in the corpus