When Can You Trust Your Synthetic Users? Diagnostics and Corrections for LLM Consumer Panels

תקציר מקורי באנגליתarXiv:2609.13148v1 Announce Type: cross Abstract: Large language models are increasingly deployed as synthetic consumer panels, promising $97\%$ cost reductions over traditional surveys. Yet aggregate validation metrics conceal systematic failures: variance compression, coefficient sign-flips, subgroup error balloons of 10--30 percentage points, and global corrections that worsen demographic bias. We provide a formal framework for deciding when to trust, correct, or abandon LLM-generated consumer data. The framework decomposes synthetic-panel bias into covariate and concept shift, develops testable diagnostics with interpretable decision thresholds, and supplies a doubly robust AIPW estimator requiring only a small calibration sample ($n = 50$-$300$). We validate on three testbeds. In cont