{"call_id":"call_00b76547","run_id":"20261015T171027Z-suite-crb_papers-naive-54c3f2/crb_papers.q100/e1a1","step":"analysis","kind":"llm","name":"llm.analyst.scripted","version":"","code_version":"2925aeab2262","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":"2026-10-15T17:10:30.287141Z","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\": \"ds_0067\",\n \"rows\": 8806,\n \"description\": \"| Variable Name | Description                                                                                                  |\\n|---------------|--------------------------------------------------------------------------------------------------------------|\\n| Unnamed: 0    | Row index from the CSV export (no substantive meaning).                                                      |\\n| con.vote_share| Conservative share of the valid vote (%) in the current constituency–election.                               |\\n| con.rv        | Conservative margin against the strongest opponent in the previous general election for the same constituency (percentage points; positive = Conservatives ahead, negative = behind). |\\n| con.winners   | Indicator for whether the Conservative candidate won the current constituency–election (1 = win, 0 = loss).  |\\n\\nData Description: \\nThis dataset assembles constituency-level results from United Kingdom general elections, organized to link each constituency’s outcome in a given election to its result in the immediately preceding election. The focus aligns with the paper’s Conservative-centered analyses and includes, for each constituency–election, the Conservative vote share, a binary win/loss indicator, and the prior-election margin versus the top opponent.\\n\\nThe data were compiled from official parliamentary election returns spanning the postwar period (approximately 1950–2010) and matched across consecutive elections at the constituency level. Constituencies subject to substantial boundary or name changes that prevented reliable linking across elections were excluded. The study examines patterns in multiparty competition—particularly where Conservatives faced Labour or Liberal/Liberal Democrat opponents—to provide context on how party competition varies across places and over time.\",\n \"columns\": {\n  \"Unnamed: 0\": \"int64\",\n  \"con.vote_share\": \"float64\",\n  \"con.rv\": \"float64\",\n  \"con.winners\": \"int64\"\n }\n}\n\n# Question\nAmong UK constituencies with very close prior elections, what is the causal effect of the Conservative party narrowly holding the seat (versus narrowly losing it) on the probability that Conservatives win the next election in that constituency?","response":"Linha de base ingênua: regressão do resultado no tratamento, sem controles.\ninfer__ols({\"dataset\": \"ds_0067\", \"y\": \"con.winners\", \"x\": [\"con.rv\"]})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_00b76547","text":"Linha de base ingênua: regressão do resultado no tratamento, sem controles.","tool_calls":[{"vendor_id":"script_1","llm_name":"infer__ols","tool_id":"infer.ols","args":{"dataset":"ds_0067","y":"con.winners","x":["con.rv"]}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"3bdc78cf55447df6d1164651b6d0756860ccb44672d47d404188a887fa6d7d8c","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["49d82a22aa469f2531ceed6676e4a3da4806c2c3ab5c78eaf44ab8b6fc5d21b8"],"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":0.7470829878002405}}
{"call_id":"call_dd35e811","run_id":"20261015T171027Z-suite-crb_papers-naive-54c3f2/crb_papers.q100/e1a1","step":"analysis","kind":"tool","name":"infer.ols","version":"0.1.0","code_version":"2925aeab2262","inputs":{"dataset":"ds_0067","y":"con.winners","x":["con.rv"],"controls":[],"fe":[],"cluster":null,"se":"HC1","weights":null,"subset":null,"intercept":true,"level":0.95},"input_digest":"f44769161606bfc1ee012057d9da131222b3f735ba3ffabe66bfb4641550fd48","dataset_digests":{"ds_0067":"a3b08f4e666cecdb4517bef53bd726ba8e0dfe244bcaf30de7a52dc1c1d70a05"},"result":{"tool":"infer.ols","version":"0.1.0","call_id":"call_dd35e811","status":"ok","reason":"","figures":[{"key":"coef.con.rv","value":0.01577182397595691,"unit":"","label":"Coefficient on con.rv in the regression of con.winners","se":0.00011200667873969493,"ci_low":0.015552264734895184,"ci_high":0.015991383217018637,"ci_level":0.95,"derivation":{"steps":[{"text_pt":"Coeficiente de con.rv na regressão por mínimos quadrados ordinários de con.winners.","formula":"","terms":[{"symbol":"n","value":8806.0,"unit":"observações","note":""},{"symbol":"k","value":2.0,"unit":"parâmetros","note":""}],"result":0.01577182397595691,"unit":""},{"text_pt":"Erro-padrão robusto à heterocedasticidade (HC1): a raiz da variância estimada do coeficiente.","formula":"ep = √V_jj","terms":[{"symbol":"V_jj","value":1.2545496082297227e-8,"unit":"","note":"diagonal da matriz de covariância"}],"result":0.00011200667873969493,"unit":""},{"text_pt":"Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 8804 graus de liberdade vezes o erro-padrão.","formula":"IC = β ± c · ep","terms":[{"symbol":"β","value":0.01577182397595691,"unit":"","note":""},{"symbol":"c","value":1.9602334747554138,"unit":"","note":"quantil 0.975"},{"symbol":"ep","value":0.00011200667873969493,"unit":"","note":""}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"n_obs","value":8806,"flag":false,"note":"observations used"},{"name":"k","value":2,"flag":false,"note":"parameters estimated, fixed-effect dummies included"},{"name":"r2","value":0.5876500464002308,"flag":false,"note":""},{"name":"df_resid","value":8804,"flag":false,"note":""},{"name":"se_type","value":"HC1","flag":false,"note":"heteroskedasticity-robust standard errors (HC1)"},{"name":"n_clusters","value":null,"flag":false,"note":"errors not clustered"}],"tables":{"coefficients":[{"term":"Intercept","coef":0.5307560819040347,"se":0.0034184664959011537,"t":155.26145496538507,"p":0.0,"ci_low":0.5240550894464394,"ci_high":0.5374570743616299},{"term":"con.rv","coef":0.01577182397595691,"se":0.00011200667873969493,"t":140.81146011489946,"p":0.0,"ci_low":0.015552264734895184,"ci_high":0.015991383217018637}]},"datasets":{},"warnings":[],"assumptions":["the conditional mean of the outcome is linear in the regressors","errors are independent across observations; their variance may differ"],"notes":""},"outputs":null,"output_digest":"b417c774082a69f1850b8a4911c644ecf2002c538fed8504221e26ef30dbb90e","params_versions":[],"sources":[],"started_at":"2026-10-15T17:10:30.289760Z","duration_ms":3.6585829220712185,"error":null,"decided_by":"model","rationale":"Linha de base ingênua: regressão do resultado no tratamento, sem controles.","cached":false,"informed_by":[],"model":null,"prompt":null,"response":null,"tokens_in":null,"tokens_out":null,"meta":{"turn_id":"call_00b76547","llm_name":"infer__ols","vendor_id":"script_1","result_for_model_ref":"e0d490d30e6ba28147b8fd7244f3a186639a20808d1f8a0b84ad8d8783c08034"}}
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