Beyond Demand Estimation: Consumer Surplus Evaluation via Cumulative Propensity Weights

2026-01-06 · Source: stat.ML updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, quick

Summary

A new framework has been developed to evaluate consumer surplus from AI-driven decisions, particularly in targeted pricing and algorithmic lending, using observational data. This approach bypasses the traditional method of estimating demand functions and then integrating them, which often faces challenges like model misspecification or slow convergence with nonparametric methods. Instead, the framework leverages the inherent randomness in modern algorithmic pricing, which arises from exploration-exploitation trade-offs. It introduces an estimator that reweights observed purchase outcomes using novel cumulative propensity weights (CPW) to reconstruct the integral of demand. An augmented cumulative propensity weighting (ACPW) estimator is also presented, offering a doubly robust variant that requires only one of either the demand model or the historical pricing policy distribution to be correctly specified. The framework extends to an inequality-aware surplus measure to quantify profit-equity trade-offs, and its methods have been validated through numerical studies.

Key takeaway

For research scientists developing or deploying AI-driven pricing and lending systems, you should consider integrating cumulative propensity weighting (CPW) or augmented CPW (ACPW) estimators into your evaluation toolkit. This allows for more robust and efficient assessment of consumer surplus and fairness impacts, bypassing the complexities of traditional demand function estimation. Adopting this framework can help quantify profit-equity trade-offs, ensuring more responsible and transparent algorithmic decision-making.

Key insights

A new framework evaluates consumer surplus from AI pricing without explicit demand function estimation.

Principles

• Exploit algorithmic randomness for unbiased demand estimates.
• Doubly robust estimators enhance model reliability.
• Incorporate fairness into surplus measurement.

Method

The method reweights observed purchase outcomes using cumulative propensity weights (CPW) to reconstruct the demand integral, avoiding explicit demand function estimation and numerical integration.

In practice

• Audit consumer surplus effects of AI pricing.
• Quantify profit-equity trade-offs.
• Use flexible ML for surplus estimation.

Topics

• Consumer Surplus Evaluation
• Algorithmic Pricing
• Propensity Weighting
• Doubly Robust Estimation
• Fairness in AI

Best for: Research Scientist, AI Researcher, AI Scientist, Data Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by stat.ML updates on arXiv.org.