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.
By Spandan Ghose Chowdhury
arXiv:2607. 18867v1 Announce Type: new Abstract: Large language models leak parametric knowledge of realized outcomes into historical financial decision tasks.
By Haozhe Jia
arXiv:2608.29420v1 Announce Type: new
Abstract: Frontier-model leaderboards now rank systems based on economic benchmarks, tests of how well models carry out professional tasks from software engineer...
By Louis Yiven Zhu
arXiv:2609.07944v1 Announce Type: new
Abstract: Existing causal-inference benchmarks for LLMs mostly score method descriptions or whether generated code runs, not whether the executed workflow recove...
By Yonghong Zhang, Ricardo Correia, Isabel M. Parra, Yong Xie
arXiv:2511. 03877v2 Announce Type: replace Abstract: Social and collaborative platforms emit multivariate time-series traces in which early interactions -- such as views, likes, or downloads -- are followed, sometimes months or years later, by higher impact like citations, sales, or reviews.
By Kimia Kazemian (Department of Computer Science, Cornell University), Zhenzhen Liu (Department of Computer Science, Cornell University), Yangfanyu Yang (Department of Information Science, Cornell University), Katie Luo (Department of Computer Science, Stanford University), Shuhan Gu (Department of Computer Science, Cornell University), Audrey Du (Department of Computer Science, Cornell University), Xinyu Yang (Department of Information Science, Cornell University), Jack Jansons (Department of Computer Science, Cornell University), Kilian Q. Weinberger (Department of Computer Science, Cornell University), John Thickstun (Department of Computer Science, Cornell University), Yian Yin (Department of Information Science, Cornell University), Sarah Dean (Department of Computer Science, Cornell University)
arXiv:2608. 20290v1 Announce Type: new Abstract: Whether a language model has improved itself is increasingly judged not by mean accuracy but by which individual problems it gains and loses.
By Cheng Xu, Nan Yan, Liming Chen, M-Tahar Kechadi
Whether a language model has improved itself is increasingly judged not by mean accuracy but by which individual problems it gains and loses. Tracking these transitions means differencing two noisy estimates, leaving them vulnerable to measurement artifacts.
arXiv:2604. 07530v2 Announce Type: replace-cross Abstract: Scaling laws describe how language model capabilities grow with compute and data, but say nothing about how long a model matters once released.
By Ana Tri\v{s}ovi\'c
arXiv:2609. 20193v1 Announce Type: new Abstract: Retrieval plug-ins supply a deep forecaster with information its lookback window cannot carry.
By Mert Onur Cakiroglu, Elham Buxton, Mehmet Dalkilic, Hasan Kurban
arXiv:2602. 16111v2 Announce Type: replace-cross Abstract: Online media platforms track the share of impressions associated with content attributes, or prevalence, to evaluate trade-offs and set guardrails in A/B experiments.
By Zehao Xu, Tony Paek, Kevin O'Sullivan, Attila Dobi
The paper introduces VINTAGE-TS, a revision‑aware time‑series foundation model that separates observation time from information‑availability time. It predicts both the next period’s first‑published value and the value available after a fixed delay, maintaining a joint distribution to capture their dependence and uncertainty. The authors provide a detailed evaluation protocol, software tools for validity‑interval reconstruction and delayed‑label filtering, and a synthetic demonstration with a 25‑configuration sensitivity suite to illustrate performance variability and the impact of hindsight contamination.
By Taimoor Ahmad
arXiv:2608. 06085v1 Announce Type: new Abstract: Survey-country metadata can improve an LLM's forecast of an individual response when informative, yet the same cue may redirect the forecast when assigned at random.
By Yifan Lyu, Xinran Li, Jiaqi Qiao, Xiujuan Xu