Reinterpreting demand estimation

2025-03-30

AI Summary

This paper bridges the theoretical gap between structural demand estimation and causal inference by clarifying how nonparametric structural assumptions in traditional demand models—such as those in Berry & Haile (2014, 2024)—can be rigorously formalized as counterfactual constraints within the Neyman–Rubin potential outcomes framework. Method: It systematically recasts key identification assumptions—specifically, those underlying market-level and demographic-segment share models—as testable counterfactual independence and homogeneity conditions within the potential outcomes model, and demonstrates that cross-market counterfactual homogeneity is necessary for identifying market-level counterfactual outcomes. Contribution: The paper establishes a precise, one-to-one correspondence between structural demand assumptions and causal identification conditions, resolves conceptual ambiguities arising from disciplinary differences in notation and terminology, and provides a unified theoretical foundation for integrating methods across structural and causal paradigms.

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Causal Learning
• Game Theory and Economic Paradigms: Mechanism Design

Application Category

• Economics, Online Markets and Human Computation: The sharing economy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

This paper connects the literature on demand estimation to the literature on causal inference by interpreting nonparametric structural assumptions as restrictions on counterfactual outcomes. It offers nontrivial and equivalent restatements of key demand estimation assumptions in the Neyman-Rubin potential outcomes model, for both settings with market-level data (Berry and Haile, 2014) and settings with demographic-specific market shares (Berry and Haile, 2024). This exercise helps bridge the literatures on structural estimation and on causal inference by separating notational and linguistic differences from substantive ones.

Problem

• Bridges demand estimation and causal inference literatures
• Reinterprets structural assumptions as counterfactual restrictions
• Highlights tradeoff between flexibility and homogeneity robustness

Innovation

• Nonparametric structural assumptions on counterfactuals
• Reformulates demand estimation in potential outcomes model
• Highlights latent homogeneity in structural demand models