arXiv Machine Learning
Across-Design Uncertainty in Short Pricing Panels: Inference and Identification
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arXiv Machine Learning
Aug 24
Across-Design Uncertainty in Short Pricing Panels: Evidence from Simulated Price Trajectories
arXiv:2608.21334v1 Announce Type: new Abstract: Short observational pricing panels can contain many observations while offering only a small number of distinct price movements. This paper studies the...
arXiv AI
Aug 28
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
Sep 24
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 Machine Learning
Jul 27
Learning Bidirectional Causal Interactions with Heteroscedastic Neural Networks
arXiv:2607. 22313v1 Announce Type: cross Abstract: Estimating contemporaneous bidirectional interactions from observational data is difficult because each outcome is endogenous to the other, while flexible regressions may capture only reduced-form dependence.
arXiv Machine Learning
Sep 22
Sparse Regression Distilled from a Single Robust Fit
arXiv:2609.23937v1 Announce Type: cross Abstract: Robust linear fits can resist response contamination yet remain too dense or unstable for useful global explanations. We propose penalized distillati...
arXiv AI
Aug 10
Blind to the Pivotal Vote: Aggregate Independence Metrics Miss Where Verification Actually Helps
arXiv:2608. 06940v1 Announce Type: new Abstract: LLM judge panels are a standard evaluation tool, but prior work reports highly correlated panel errors: nine judges provide roughly the effective information of two independent ones, and aggregation closes only a small fraction of the gap.