Reporting Guidelines for Meta-Analysis in Economics, updated for AI, just published in the Journal of…

Reporting Guidelines for Meta-Analysis in Economics, updated for AI, just published in the Journal of Economic Surveys:
https://lnkd.in/d_BciGAd

Two practical points I would emphasize (my personal opinion), beyond the reporting checklist itself:

1️⃣ If you use AI for searching, screening, or coding, don't rely on a single model. Use meta-analysis thinking: each model is trained differently and on different data (think Claude vs. Grok). Even if one model strictly dominates, there will be useful information in the others, and you need to stress-test your favorite model brutally regardless. We have developed a simple Research Audit Protocol based on Multi-Agent Debate (MAD) for exactly this:
https://lnkd.in/d63DFbPz

2️⃣ These guidelines intentionally do not recommend any particular methodology. We do so in our 2024 method guidelines (https://lnkd.in/dPcDR7Gr). Brief update: I think the baseline meta-analysis technique is now Robust Bayesian Meta-Analysis (RoBMA) by František Bartoš, Maximilian Maier, and Eric-Jan Wagenmakers -- a principled way to average over various bias-correction methods. But these methods don't address p-hacking, so RoBMA should be complemented with MAIVE (easy to apply via https://easymeta.org) and RTMA (Maya Mathur).

The updated reporting guidelines were led by Nikolai Cook and co-authored with František Bartoš, Pedro Bom, Sebastian Gechert, Klára Kantová, Jerome Geyer-Klingeberg, Dr.-Ing., Tomas Havranek, Martina Lušková, Matej Opatrny, Franz Prante, Heiko Rachinger, and Tom Stanley.