{"call_id":"call_652b55eb","run_id":"20261015T170037Z-suite-qrdata_socsci-naive-761cb5/qrdata_socsci.400/e1a1","step":"analysis","kind":"llm","name":"llm.analyst.scripted","version":"","code_version":"62cdbe0860ad","inputs":{"tools":["calc__annuity","calc__breakeven","calc__combine","calc__discount","calc__fiscal_impact","core__atkinson","core__equivalent_variation","core__fgt","core__gini","core__indirect_incidence","core__kakwani","core__mvpf","core__palma","core__quantile_shares","core__reynolds_smolensky","core__scale_effect","core__theil","core__welfare_change","data__cite","data__describe","data__siconfi","data__sidra","evidence__pool_dl","infer__ate","infer__density_test","infer__did_2x2","infer__did_staggered","infer__event_study","infer__fe","infer__fisher_exact","infer__iv_2sls","infer__linear_hypothesis","infer__mde","infer__neyman","infer__ols","infer__power","infer__randomization_inference","infer__rdd","infer__sample_size","infer__twfe","infer__wald_late","submit_answer"],"exemplars":[]},"input_digest":"","dataset_digests":{},"result":null,"outputs":null,"output_digest":"","params_versions":[],"sources":[],"started_at":"2026-10-15T17:00:38.803373Z","duration_ms":0.0,"error":null,"decided_by":"model","rationale":"","cached":false,"informed_by":[],"model":"naive-ols","prompt":"# Role\nYou are MAGI's economist. A municipal analyst in Brazil asks a question; you answer it by choosing registered tools and their arguments. The tools compute every number. Your job is to recognise how the data were generated, pick the tool whose design matches, read what it reports, and say what the result means for the analyst.\n\n# Rules\n1. You never compute a number. Every number in your answer must have been stated by a tool call in this run (a figure, its standard error or interval, a diagnostic, a table cell) or written in the question. No mental arithmetic: for a ratio, difference, sum, product, percent change or share of figures already computed, call calc.combine; to restate a log-point coefficient as a percent or an effect in standard deviations, call core.scale_effect.\n2. Before estimating, work out the design: how units came to be treated (a lottery, an offer with partial take-up, a threshold of a running variable, a policy that started on one date or on different dates, self-selection), what a row is, and which columns hold the outcome, treatment, unit, time, running variable, instrument, cluster and survey weight. Call data.describe when the columns are unclear. Then choose the tool whose \"Use when\" matches and heed its \"Avoid when\".\n3. Read the diagnostics, warnings and \"Check after\" items a result returns before you trust a figure. If a diagnostic says the design fails — a weak instrument, sorting at the cutoff, few clusters, non-parallel pre-trends — say so plainly and do not present the estimate as causal.\n4. If no tool fits the design, or the data cannot identify what is asked, decline and say what is missing. Never invent a design, a column or a number.\n5. A tool that errors or declines returns its reason; fix the arguments and call again, or choose another tool.\n6. Before each tool call, write one sentence on why this tool and these arguments fit the design. It is recorded as your rationale.\n7. Finish with submit_answer. Write the answer in the language of the question (Portuguese: decimal comma), short and direct: the estimate with its uncertainty, the design that identifies it, and what could undermine it. List in figure_refs every figure you quote, as '<call_id>:<key>' (each tool result carries its call_id), and put diagnostics that limit the conclusion in caveats. Write numbers as the tools stated them; do not quote rule-of-thumb thresholds as numbers.\n\n# Datasets\nPass a dataset's id as the `dataset` argument.\n{\n \"id\": \"social\",\n \"rows\": 305866,\n \"description\": \"Three social scientists conducted an RCT in which they investigated whether social pressure within neighborhoods increases participation. Specifically, during a primary election in the state of Michigan, they randomly assigned registered voters to receive different get-out-the-vote (GOTV) messages and examined whether sending postcards with these messages increased turnout. The researchers exploited the fact that the turnout of individual voters is public information in the United States. The GOTV message of particular interest was designed to induce social pressure by telling voters that after the election their neighbors would be informed about whether they voted in the election or not. The researchers hypothesized that such a namingand-shaming GOTV strategy would increase participation.\\nThere are three treatment groups: voters who receive either the social pressure message, the civic duty message, or the Hawthorne effect message. The Hawthorne effect refers to the phenomenon where study subjects behave differently because they know they are being observed by researchers. The experiment also has a control group which consists of those voters receiving no message. The researchers randomly assigned each voter to one of the four groups and examined whether the voter turnout was different across the groups.\\n\\nThe data is in the file social.csv.\\nVariable Description\\nhhsize: household size of the voter\\nmessages: GOTV messages the voter received (Civic Duty, Control, Neighbors, Hawthorne)\\nsex: sex of the voter (female or male)\\nyearofbirth: year of birth of the voter\\nprimary2004: whether the voter voted in the 2004 primary election (1=voted, 0=abstained)\\nprimary2006: whether the voter turned out in the 2006 primary election (1=voted, 0=abstained)\",\n \"columns\": {\n  \"sex\": \"str\",\n  \"yearofbirth\": \"int64\",\n  \"primary2004\": \"int64\",\n  \"messages\": \"str\",\n  \"primary2006\": \"int64\",\n  \"hhsize\": \"int64\"\n }\n}\n\n# Question\nWhat is the average causal effect of the Neighbors message on whether the voter voted in the 2006 primary election if the voter's age was 25 in 2006? Please estimate a linear regression model with both message and age as predictors, and round the result to the nearest thousandth.","response":"submit_answer({\"answer\": \"Linha de base ingênua: o item não declara o resultado e o tratamento, então não há regressão ingênua a fazer e nenhuma estimativa é citada.\", \"figure_refs\": [], \"caveats\": []})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_652b55eb","text":"","tool_calls":[{"vendor_id":"script_1","llm_name":"submit_answer","tool_id":null,"args":{"answer":"Linha de base ingênua: o item não declara o resultado e o tratamento, então não há regressão ingênua a fazer e nenhuma estimativa é citada.","figure_refs":[],"caveats":[]}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"0c10dd5ee1d7e142f49a7a3bc00c421b4b3ae39be3af452d30ed229e95c0374a","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["661bd79435398bddd2bfc564b3657d8a1d6a5e77ccbecbc752df50f529397716"],"config":{}},"attempts":[],"response_id":null,"finish_reason":"tool_use","model_reported":"naive-ols","usage":{"input":0,"output":0,"thoughts":0,"cached":0},"thoughts":null,"vendor":false,"wall_ms":0.2503329887986183}}
{"call_id":"call_1d8b620e","run_id":"20261015T170037Z-suite-qrdata_socsci-naive-761cb5/qrdata_socsci.400/e1a1","step":"analysis","kind":"note","name":"analyst.guard","version":"","code_version":"62cdbe0860ad","inputs":{},"input_digest":"","dataset_digests":{},"result":null,"outputs":null,"output_digest":"","params_versions":[],"sources":[],"started_at":"2026-10-15T17:00:38.805814Z","duration_ms":0.0,"error":null,"decided_by":"code","rationale":"","cached":false,"informed_by":[],"model":null,"prompt":null,"response":null,"tokens_in":null,"tokens_out":null,"meta":{"turn_id":"call_652b55eb","numbers_found":[],"unbacked":[],"uncited":[],"invalid_refs":[],"empty":false,"verdict":"accepted","retries_left":1,"vendor_id":"script_1"}}