{"call_id":"call_6eca2377","run_id":"20261015T171059Z-suite-causcibench_real-scripted-ded01d/causcibench_real.q085/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":"2026-10-15T17:11:01.368862Z","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\": \"urdinez_undermining\",\n \"rows\": 4445,\n \"description\": \"The data comes from an online panel survey conducted across 6 countries: Argentina, Brazil, Chile, Colombia, Mexico, and Peru. The survey was conducted in two waves, before Chinese COVID-19 vaccines were available and after vaccination campaigns were being carried out extensively. In the first round, the survey gathered socio-demographic information and participants' views on the six countries. Meanwhile, the second wave contained vaccine-corrected questions, such as whether respondents had received the Chinese Covid-19 vaccine. Both surveys asked the participants' attitudes towards China and the USA.\\nData variables:\\nwave: survey year (2020, 2021)\\nduration: time spent by respondents in the survey\\ncountry: numeric country code\\ndevice: survey device used\\nsex: 0=female, 1=male\\nage: respondents' age\\nagerecode: age group code\\nvaccine: vaccination status\\nsouth_korea_score: favorability score assigned to South Korea\\nchina_score: favorability score assigned to China\\nusa_score: favorability score assigned to the U.S.\\njapan_score: favorability score assigned to Japan\\nideology: political ideology\\neducation: highest education level\\nregion_ar: Argentina subregion code\\nregion_br: Brazil subregion code\\nregion_co: Colombia subregion code\\nregion_cl: Chile subregion code\\nregion_mx: Mexico subregion code\\nregion_pe: Peru subregion code\\ndelta_china: change in favorability score for China\\ndelta_usa: change in favorability score for the U.S.\\nincome: respondents' income\\nregion_code: numeric region identifier\\ncountry_name: name of the country\\nrespondent_code: unique respondent identifier\\nnews_experiment: treatment in survey experiment\\nboth_waves: indicator if respondent answered both waves\\nexcluded: indicator for excluded observations\",\n \"columns\": {\n  \"wave\": \"int64\",\n  \"duration\": \"int64\",\n  \"country\": \"int64\",\n  \"device\": \"int64\",\n  \"sex\": \"int64\",\n  \"age\": \"int64\",\n  \"agerecode\": \"int64\",\n  \"vaccine\": \"int64\",\n  \"south_korea_score\": \"float64\",\n  \"china_score\": \"int64\",\n  \"usa_score\": \"int64\",\n  \"japan_score\": \"float64\",\n  \"ideology\": \"int64\",\n  \"education\": \"int64\",\n  \"region_ar\": \"float64\",\n  \"region_br\": \"float64\",\n  \"region_co\": \"float64\",\n  \"region_cl\": \"float64\",\n  \"region_mx\": \"float64\",\n  \"region_pe\": \"float64\",\n  \"delta_china\": \"float64\",\n  \"delta_usa\": \"float64\",\n  \"income\": \"int64\",\n  \"region_code\": \"int64\",\n  \"country_name\": \"str\",\n  \"respondent_code\": \"int64\",\n  \"news_experiment\": \"int64\",\n  \"both_waves\": \"int64\",\n  \"excluded\": \"int64\"\n }\n}\n\n# Question\nDoes receiving a Chinese COVID-19 vaccine cause an increase in an individual's favorability toward China?","response":"causcibench_metadata_rule/1: did\ninfer__ols({\"dataset\": \"urdinez_undermining\", \"y\": \"china_score\", \"x\": [\"vaccine\"], \"controls\": [], \"fe\": [\"respondent_code\", \"wave\"]})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_6eca2377","text":"causcibench_metadata_rule/1: did","tool_calls":[{"vendor_id":"script_1","llm_name":"infer__ols","tool_id":"infer.ols","args":{"dataset":"urdinez_undermining","y":"china_score","x":["vaccine"],"controls":[],"fe":["respondent_code","wave"]}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"9f7130375e58cf06164380c723b1f32eb13ce915840d15bacd2bfa1348d64178","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["4e288d2ca8fa6a2d7a4743378e944d5fdda3eed27146ff1e4e3bc4caf9f415b0"],"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":0.79541583545506}}
{"call_id":"call_ddcf679b","run_id":"20261015T171059Z-suite-causcibench_real-scripted-ded01d/causcibench_real.q085/e1a1","step":"analysis","kind":"tool","name":"infer.ols","version":"0.1.0","code_version":"2925aeab2262","inputs":{"dataset":"urdinez_undermining","y":"china_score","x":["vaccine"],"controls":[],"fe":["respondent_code","wave"],"cluster":null,"se":"HC1","weights":null,"subset":null,"intercept":true,"level":0.95},"input_digest":"27115422e92fec8b8c8c1cee8d662c15887765fd2dfaaada16f0842a99cd98f4","dataset_digests":{"urdinez_undermining":"be8c21837f47851acc322a376c4d699f08336d9143f56b79277184ea265367ee"},"result":{"tool":"infer.ols","version":"0.1.0","call_id":"call_ddcf679b","status":"ok","reason":"","figures":[{"key":"coef.vaccine","value":3.9753489844147873,"unit":"","label":"Coefficient on vaccine in the regression of china_score","se":1.9562961797160763,"ci_low":0.13814688802713304,"ci_high":7.812551080802441,"ci_level":0.95,"derivation":{"steps":[{"text_pt":"Coeficiente de vaccine na regressão por mínimos quadrados ordinários de china_score, efeitos fixos de respondent_code, wave.","formula":"","terms":[{"symbol":"n","value":4445.0,"unit":"observações","note":""},{"symbol":"k","value":2861.0,"unit":"parâmetros","note":""}],"result":3.9753489844147873,"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":3.8270947427717146,"unit":"","note":"diagonal da matriz de covariância"}],"result":1.9562961797160763,"unit":""},{"text_pt":"Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 1584 graus de liberdade vezes o erro-padrão.","formula":"IC = β ± c · ep","terms":[{"symbol":"β","value":3.9753489844147873,"unit":"","note":""},{"symbol":"c","value":1.9614627560866371,"unit":"","note":"quantil 0.975"},{"symbol":"ep","value":1.9562961797160763,"unit":"","note":""}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"n_obs","value":4445,"flag":false,"note":"observations used"},{"name":"k","value":2861,"flag":false,"note":"parameters estimated, fixed-effect dummies included"},{"name":"r2","value":0.7837543883864699,"flag":false,"note":""},{"name":"df_resid","value":1584,"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":"respondent_code (2859 levels), wave (2 levels)","flag":false,"note":"absorbed as dummies; their coefficients are not tabulated"}],"tables":{"coefficients":[{"term":"Intercept","coef":27.91725105189341,"se":26.167078780058173,"t":1.0668845111273573,"p":0.28618656789293045,"ci_low":-23.408499410775654,"ci_high":79.24300151456248},{"term":"vaccine","coef":3.9753489844147873,"se":1.9562961797160763,"t":2.032079306616927,"p":0.04231220031595482,"ci_low":0.13814688802713304,"ci_high":7.812551080802441}]},"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":"dddcd534f5e3af4d339ae7656f34eb391d7bc28841ecc37dd1cfd59c107d2fb9","params_versions":[],"sources":[],"started_at":"2026-10-15T17:11:01.371394Z","duration_ms":2389.6136248949915,"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_6eca2377","llm_name":"infer__ols","vendor_id":"script_1","result_for_model_ref":"1640972145ca3422ae4c0eba76a8b3bff6d7e9e1f7a1f2b83a804a801b1cc4e0"}}
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