Beyond the Illusion of Power: Calibrating Quasi-Experiments in Observational IS
Read the original on arXiv AI →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.
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