arXiv Machine Learning
Across-Design Uncertainty in Short Pricing Panels: Evidence from Simulated Price Trajectories
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arXiv Machine Learning
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Across-Design Uncertainty in Short Pricing Panels: Inference and Identification
arXiv:2608.21334v2 Announce Type: replace Abstract: Short observational pricing panels often contain many observations but few distinct price movements. We evaluate the inferential consequences of th...
arXiv AI
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LLM-Powered Virtual Population for Demand Simulation and Pricing
arXiv:2606. 16183v1 Announce Type: cross Abstract: We develop an LLM-powered virtual population model that simulates demand for pricing decisions, in settings where products are described by rich unstructured information, such as text descriptions and images, and where decision makers need not only mean-demand predictions but also uncertainty estimates for counterfactual prices.
arXiv AI
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Beyond the Illusion of Power: Calibrating Quasi-Experiments in Observational IS
Information systems researchers increasingly rely on quasi‑experimental methods such as difference‑in‑differences and instrumental variables to infer causal effects from observational panel data. A large Monte Carlo study of 9,837 parameter settings (≈9.8 million simulated datasets) shows that the gap between planned and achieved power is largely driven by serial correlation, panel attrition, staggered adoption bias, and parallel‑trend pre‑testing—factors that no closed‑form power calculator can fully capture. For IV designs, increasing sample size does not improve power or reduce exclusion bias unless instrument strength is enhanced, underscoring that identification hinges on the instrument rather than on larger N.
arXiv Statistics ML
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Uncertainty and Explainability in Deep Rough Volatility: A Neural Information-Theoretic Posterior Approach
arXiv:2609.31570v1 Announce Type: new Abstract: Deep learning has substantially accelerated the calibration of complex stochastic-volatility models, but neural point calibration alone does not captur...
arXiv AI
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Selection Bias Correction in Retail Intelligence
The paper examines how retail intelligence, which often focuses on high‑velocity products, can suffer from selection bias that skews inflation estimates by overlooking niche items. Using 400 Monte Carlo simulations across four data‑generating scenarios, the authors compare Inverse Probability Weighting (IPW) and stratification methods. They find that stratification generally outperforms IPW—achieving sub‑0.04 percentage‑point median error even when population breaks misalign—while IPW only excels under smooth polynomial relationships, highlighting the importance of method choice in long‑tail retail contexts.
arXiv AI
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RouteCost: A Production-Inspired Multi-Stage Framework for Pre-Order Shipping Cost Estimation in E-Commerce
arXiv:2607. 16230v1 Announce Type: cross Abstract: Accurate pre-order shipping cost estimation is important in e-commerce because it affects price presentation, margin planning, and conversion.