{"call_id":"call_9c1c6955","run_id":"20261015T171059Z-suite-causcibench_real-scripted-ded01d/causcibench_real.q083/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":"2026-10-15T17:11:01.278989Z","duration_ms":0.0,"error":null,"decided_by":"model","rationale":"","cached":false,"informed_by":[],"model":"reference","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\": \"rickard_incumbents\",\n \"rows\": 1890,\n \"description\": \"The dataset tracks municipalities in Spain across multiple election cycles to study the effect of local offshoring events on incumbent vote shares. Offshoring events were identified using corporate balance sheet databases, prior academic compilations, media reports, and public restructuring monitors. Each event was verified through at least two independent sources to ensure accuracy. For every confirmed event, the exact plant location and the number of jobs lost were recorded. These events were then matched with municipality-level election results to capture changes in incumbent party vote shares across elections. Additional contextual variables, such as population, unemployment, and economic growth, were obtained from official regional statistical agencies.\\n\\nData variables:\\ncodi: Municipality code\\nyear: Election year\\nspp: Incumbent party vote share (%)\\npostp: Post-treatment period indicator\\ntreatment_postp: Treatment _ Post period indicator\\nintensity_postp: Number of jobs lost due to offshoring\\npop_thou: Population in thousands\\nurate: Local unemployment rate (%)\\nyrrate: Local economic growth rate (%)\",\n \"columns\": {\n  \"codi\": \"int64\",\n  \"year\": \"float64\",\n  \"spp\": \"float64\",\n  \"postp\": \"float64\",\n  \"treatment_postp\": \"float64\",\n  \"intensity_postp\": \"float64\",\n  \"yrrate\": \"float64\",\n  \"pop_thou\": \"float64\",\n  \"urate\": \"float64\"\n }\n}\n\n# Question\nDoes experiencing an offshoring event cause a reduction in the incumbent government party's (PP) vote share in Catalan municipalities between the 2000 and 2004 elections?","response":"causcibench_metadata_rule/1: did\ninfer__ols({\"dataset\": \"rickard_incumbents\", \"y\": \"spp\", \"x\": [\"treatment_postp\"], \"controls\": [], \"fe\": [\"codi\", \"year\"]})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_9c1c6955","text":"causcibench_metadata_rule/1: did","tool_calls":[{"vendor_id":"script_1","llm_name":"infer__ols","tool_id":"infer.ols","args":{"dataset":"rickard_incumbents","y":"spp","x":["treatment_postp"],"controls":[],"fe":["codi","year"]}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"01e2c24d24cb59c42314fea4b54bfecf8f71e571bfe15dc7322a77d82b8c023c","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["1877bc1bb0b0f5997011f8f003e73189cf5cbbec6cf03d6df61c11869771d5b9"],"config":{}},"attempts":[],"response_id":null,"finish_reason":"tool_use","model_reported":"reference","usage":{"input":0,"output":0,"thoughts":0,"cached":0},"thoughts":null,"vendor":false,"wall_ms":0.7474159356206656}}
{"call_id":"call_ca201e3f","run_id":"20261015T171059Z-suite-causcibench_real-scripted-ded01d/causcibench_real.q083/e1a1","step":"analysis","kind":"tool","name":"infer.ols","version":"0.1.0","code_version":"2925aeab2262","inputs":{"dataset":"rickard_incumbents","y":"spp","x":["treatment_postp"],"controls":[],"fe":["codi","year"],"cluster":null,"se":"HC1","weights":null,"subset":null,"intercept":true,"level":0.95},"input_digest":"7cc8b21c8d3cb4b34c25735e93feb71b0df7b16d47ea6f8d4418c7279fc36694","dataset_digests":{"rickard_incumbents":"96e74d9d15dfe76135972255aaa0bec719abcf8f25cb7c3e1d9480320741f826"},"result":{"tool":"infer.ols","version":"0.1.0","call_id":"call_ca201e3f","status":"ok","reason":"","figures":[{"key":"coef.treatment_postp","value":-1.9466891012147194,"unit":"","label":"Coefficient on treatment_postp in the regression of spp","se":0.4633039609710343,"ci_low":-2.8559188968487614,"ci_high":-1.0374593055806773,"ci_level":0.95,"derivation":{"steps":[{"text_pt":"Coeficiente de treatment_postp na regressão por mínimos quadrados ordinários de spp, efeitos fixos de codi, year.","formula":"","terms":[{"symbol":"n","value":1885.0,"unit":"observações","note":""},{"symbol":"k","value":945.0,"unit":"parâmetros","note":""}],"result":-1.9466891012147194,"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":0.21465056025144968,"unit":"","note":"diagonal da matriz de covariância"}],"result":0.4633039609710343,"unit":""},{"text_pt":"Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 940 graus de liberdade vezes o erro-padrão.","formula":"IC = β ± c · ep","terms":[{"symbol":"β","value":-1.9466891012147194,"unit":"","note":""},{"symbol":"c","value":1.9624908747345824,"unit":"","note":"quantil 0.975"},{"symbol":"ep","value":0.4633039609710343,"unit":"","note":""}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"n_obs","value":1885,"flag":false,"note":"observations used"},{"name":"k","value":945,"flag":false,"note":"parameters estimated, fixed-effect dummies included"},{"name":"r2","value":0.9292575559955834,"flag":false,"note":""},{"name":"df_resid","value":940,"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"},{"name":"fixed_effects","value":"codi (943 levels), year (2 levels)","flag":false,"note":"absorbed as dummies; their coefficients are not tabulated"}],"tables":{"coefficients":[{"term":"Intercept","coef":26.27436611111111,"se":0.3134980564377004,"t":83.8102998457742,"p":0.0,"ci_low":25.659129036105096,"ci_high":26.889603186117125},{"term":"treatment_postp","coef":-1.9466891012147194,"se":0.4633039609710343,"t":-4.20175363304616,"p":0.000029008631838855637,"ci_low":-2.8559188968487614,"ci_high":-1.0374593055806773}]},"datasets":{},"warnings":["5 rows were dropped because one of 'spp', 'treatment_postp', 'codi', 'year' was missing"],"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":"b115b7ca9774ff03693bdc96ab5a4354f5789d2679cb56e7ce152e22621034f8","params_versions":[],"sources":[],"started_at":"2026-10-15T17:11:01.281653Z","duration_ms":151.3397090602666,"error":null,"decided_by":"model","rationale":"causcibench_metadata_rule/1: did","cached":false,"informed_by":[],"model":null,"prompt":null,"response":null,"tokens_in":null,"tokens_out":null,"meta":{"turn_id":"call_9c1c6955","llm_name":"infer__ols","vendor_id":"script_1","result_for_model_ref":"2ccd05331e0c6f7d746e76726163ac504d2b3580182527f4e518395d460d0e24"}}
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