Meta-Analysis Instrumental Variable Estimator with easymeta.org, AI guidelines for meta-analyses, and the AI multi-agent/duel protocol. 23 September 2026, Tomas Havranek.

Draft: prepared for the workshop and not yet reviewed. The final materials will replace this page after 23 September 2026.

Demo data

Download electricity_sr.csv

Short-run price elasticities of electricity demand: 1,647 estimates from 226 studies, in the columns effect, se, n_obs and study_id. From Kudela, Havranek, Irsova, Kudelova and Sikl (2026).

Upload it at easymeta.org and choose WLS; the method is already PET-PEESE. Advanced Options opens by itself (clicking its title closes it): drag Winsorization to 2.5% and run. You should get -0.127. For EK, press the browser Back button, set Method to EK and run again: -0.105. Do not reload the model page (F5): it drops the data.

For MAIVE, start again at easymeta.org and upload the file again: the browser Back button keeps the old settings. Choose MAIVE and open Advanced Options (on a fresh MAIVE form it starts closed), set the log first stage to Yes and winsorization to 2.5%. Before you run, MAIVE Method should read PET-PEESE and Weighting Equal Weights; if not, set them. You should get -0.243, with a first-stage F of 25.

WAIVE (experimental) and RDT are in the same Model Type list. RDT runs only in the app.

Run it from an AI assistant

This part is optional: it runs on the screen during the workshop. The prompts need an assistant that can run code with internet access, for example Claude Code or Codex; ordinary chat windows can only write the code for you. Start the assistant in the folder where you saved electricity_sr.csv.

1. PET-PEESE and the endogenous kink · 01_petpeese_ek.txt

Use the EasyMeta API, not your own estimation. Data: electricity_sr.csv in this folder
(columns effect, se, n_obs, study_id). Send every row unchanged to
POST https://api.maive.eu/v1/run-model with curl or Python requests (not urllib), twice:
{"recipe":"PET-PEESE","parameters":{"winsorize":2.5},"data":[...]} and the same with "recipe":"EK".
For each run show the HTTP status, the number of rows sent, effectEstimate and standardError
exactly as returned, and resolvedParameters. If a call fails, show the error and stop.
Never report a number the API did not return.

"recipe" is the one setting that goes outside "parameters"; anything else put there is ignored.

2. MAIVE · 02_maive.txt

Use the EasyMeta API, not your own estimation. Data: electricity_sr.csv in this folder
(columns effect, se, n_obs, study_id). Send every row unchanged, with curl or Python requests
(not urllib), as POST https://api.maive.eu/v1/run-model with the JSON body
{"data":[{"effect":...,"se":...,"n_obs":...,"study_id":...}, ...], "parameters":{...}}
and these parameters (settings outside "parameters" are ignored):
{"modelType":"MAIVE","maiveMethod":"PET-PEESE","weight":"equal_weights","useLogFirstStage":true,
"standardErrorTreatment":"clustered_cr2","includeStudyClustering":true,"computeAndersonRubin":false,
"winsorize":2.5}
Show the HTTP status, rows sent, effectEstimate, standardError, firstStageFStatistic and
hausmanTest exactly as returned, and confirm that resolvedParameters.modelType
is MAIVE and useLogFirstStage is true. If the call fails, show the error and stop.
Never report a number the API did not return.

For WAIVE (experimental), ask the assistant to repeat the call with "modelType":"WAIVE". Until MAIVE issue #30 is fixed you get -0.191; after the fix, about -0.28. RDT is not in the API.

3. MAIVE in one line · 03_oneliner.txt

Run MAIVE on this dataset, following the protocol at https://meta-analysis.cz/maive/how-to/#ai

This one works on the small example on the MAIVE how-to page. On a file the size of electricity_sr.csv it can run past the server's two-minute limit, because the protocol asks for bootstrap standard errors. For this file, use prompt 2.

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