Methods, data, and code for meta-analysis
This site collects meta-analysis methods and applications by Tomas Havranek and Zuzana Irsova of Charles University, Prague, and their co-authors. Nearly every paper here comes with its data and estimation code, and every one is also republished in full as HTML. Papers collected here have appeared in journals such as Nature, Review of Economics and Statistics, and Journal of the European Economic Association. They have been cited more than 10,000 times on Google Scholar’s count, including by five Nobel laureates, in Science and PNAS, and in the research of the IMF, the ECB, and the Federal Reserve: who has used this work.
A new meta-analysis approach robust to p-hacking:
Meta-Analysis Instrumental Variable Estimator (MAIVE) — Irsova, Bom, Havranek & Rachinger, Nature Communications, 2025
One-click meta-analysis in your browser, with corrections for p-hacking:
EasyMeta.org, which runs MAIVE, RTMA, and standard models — start with the worked example
Nontechnical, step-by-step guidelines on how to do a meta-analysis:
The Practitioner’s Guide to Modern Meta-Analysis
Two 2026 notes in the Journal of Economic Surveys extend it: reporting guidelines updated for AI and a floor on the use of AI in meta-analysis.
Three principles of meta-analysis, as of 2026:
- Correct for publication bias. RoBMA, by Bartos, Maier, and Wagenmakers.
- Correct for p-hacking. MAIVE (how to run it), by Irsova et al., and Mathur’s RTMA.
- Cluster by study. CR2 standard errors, by Pustejovsky and Tipton.
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