Pedro Cadahia
arXiv 8 Sep 2026 · Econometrics
arXiv:2609.10615 · PDF · Extracted main text
This paper presents a causal decision-making framework for estimating price elasticity in retail channels, a process typically confounded by promotions, competitor movements, and market frictions. Rather than forcing a calculation when data is ambiguous, the system introduces decision abstention (\textsc{wait}) as an active diagnostic tool rather than an estimation failure. Combining Double Machine Learning and conformal prediction, the tool evaluates whether reliable conditions exist to adjust prices or if pausing the decision is preferable. When the system abstains, it exhaustively classifies the reason for the pause, identifying which products require designed pricing experiments or whether aggregating data to the brand level restores usable estimates. Tested on controlled synthetic data, the model shows that this operational discipline drastically reduces estimation error (lowering RMSE from 0.571 to 0.159) and offers a practical, secure alternative to blind estimation in thin-data retail environments.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Pedro Cadahia Delgado (2026) Across-Design Uncertainty in Short Pricing Panels: inference and Identification | 0.946 | 13 | 6 | 85% |
| 2 | Deng, Yuming and Zhang, Xinhui and Wang, Tong and Wang, Lin and Zhan… (2023) Alibaba realizes millions in cost savings through integrated demand forecasting, inventory management, price optimization, and p… | 0.928 | 5 | 4 | 80% |
| 3 | Llenas, Aleix and Salazar-Treviño, Eduardo and Leskovar, Francisco a… (2026) PepsiCo deploys AI-driven pricing and promotion optimization at scale | 0.874 | 6 | 4 | 67% |
| 4 | Barber, Rina Foygel and Candès, Emmanuel J. and Ramdas, Aaditya and… (2023) Conformal prediction beyond exchangeability | 0.843 | 4 | 4 | 75% |
| 5 | Gibbs, Isaac and Candès, Emmanuel (2021) Adaptive conformal inference under distribution shift | 0.843 | 4 | 4 | 75% |
| 6 | Hormby, Sharon and Morrison, Julia and Dave, Prashant and Meyers, Mi… (2010) Marriott International increases revenue by implementing a group pricing optimizer | 0.843 | 3 | 3 | 100% |
| 7 | Chernozhukov, Victor and Chetverikov, Denis and Demirer, Mert and Du… (2018) Double/debiased machine learning for treatment and structural parameters | 0.794 | 6 | 5 | 50% |
| 8 | Paule, Robert C. and Mandel, John (1982) Consensus values and weighting factors | 0.737 | 5 | 4 | 40% |
| 9 | Dietvorst, Berkeley J. and Simmons, Joseph P. and Massey, Cade (2015) Algorithm aversion: people erroneously avoid algorithms after seeing them err | 0.737 | 4 | 4 | 50% |
| 10 | Dietvorst, Berkeley J. and Simmons, Joseph P. and Massey, Cade (2018) Overcoming algorithm aversion: people will use imperfect algorithms if they can (even slightly) modify them | 0.737 | 4 | 4 | 50% |
Showing the top 10 of 78 scored citations.