{"call_id":"call_c116708c","run_id":"20261015T171059Z-suite-causcibench_real-scripted-ded01d/causcibench_real.q087/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":"2026-10-15T17:11:01.341878Z","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_china\",\n \"rows\": 297,\n \"description\": \"This dataset contains information on 537 donations across 33 Latin American and Caribbean countries between 11 February and 20 June 2020. It details China's and Taiwan's mask diplomacy, including donors, recipients, donated items, and the political determinants of aid. The data highlight the influence of strategic partnerships, the One China Policy, and regime type in allocation decisions.\\nData variables:\\ncow_code: country code\\ndonor code: Donor code (1 = central government, 2 = provincial government, 3 = municipal government, 4 = university, 5 = enterprise, 6 = individual, 7 = foundation, 8 = other, 100 = Taiwan)\\namount_usd: Total value of donations (in millions)\\ncountry: Name of the recipient country\\niso_code: iso_code of the country\\nstr_partnership: Partnership status with China (0 = No partnership, 1 = Partnership, 2 = Strategic Partnership, 3 = Comprehensive Strategic Partnership)\\ntaiwan: dummy variable that is 1 if the country has diplomatic relations with Taiwan\\ngdp_per_capita_log: the logarithm of gdp per capita\\ntotal_deaths_log: total deaths due to COVID-19\\nchinese_exports: total Chinese exports\\nchinese_exports_log: logarithm of the total Chinese exports to the country\\ndemocracy: Discrete variable for democracy status (0 = Autocracy; 1 = Anocracy; 2 = Weak democracy; 3 = Full democracy)\\namount_usd_log: logarithm of total donations\\ncustoms: total customs\\ncustoms_log: logarithm of total customs\\nagree_us: Measure of affinity with the United States\\nagree_cn: Measure of affinity with China\\ntotal_china: Total donations from China (in millions)\\ntotal_china_ln: Logarithm of total donations from China\",\n \"columns\": {\n  \"Unnamed: 0\": \"int64\",\n  \"cow_code\": \"float64\",\n  \"donor_code\": \"int64\",\n  \"amount_usd\": \"float64\",\n  \"country\": \"str\",\n  \"iso_code\": \"str\",\n  \"str_partnership\": \"int64\",\n  \"taiwan\": \"int64\",\n  \"gdp_per_capita_log\": \"float64\",\n  \"total_deaths_log\": \"float64\",\n  \"chinese_exports\": \"float64\",\n  \"chinese_exports_log\": \"float64\",\n  \"democracy\": \"float64\",\n  \"amount_usd_log\": \"float64\",\n  \"customs\": \"float64\",\n  \"customs_log\": \"float64\",\n  \"agree_us\": \"float64\",\n  \"agree_cn\": \"float64\",\n  \"total_china\": \"float64\",\n  \"total_china_ln\": \"float64\"\n }\n}\n\n# Question\nDoes having a strategic partnership with China increase the total Chinese foreign aid a country receives during COVID-19?","response":"causcibench_metadata_rule/1: ols\ninfer__ols({\"dataset\": \"urdinez_china\", \"y\": \"total_china_ln\", \"x\": [\"str_partnership\"], \"controls\": [\"democracy\", \"taiwan\", \"agree_us\", \"chinese_exports_log\", \"total_deaths_log\", \"gdp_per_capita_log\"]})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_c116708c","text":"causcibench_metadata_rule/1: ols","tool_calls":[{"vendor_id":"script_1","llm_name":"infer__ols","tool_id":"infer.ols","args":{"dataset":"urdinez_china","y":"total_china_ln","x":["str_partnership"],"controls":["democracy","taiwan","agree_us","chinese_exports_log","total_deaths_log","gdp_per_capita_log"]}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"eb312f258473bd1227c25ef79ef36752dc8369b8ac22e6db71aef894850a935e","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["8f1f760d8057658bbb4f6d132d420fd2de71aadf08add4e0caed0f556d9f59bc"],"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":0.6780000403523445}}
{"call_id":"call_555e513e","run_id":"20261015T171059Z-suite-causcibench_real-scripted-ded01d/causcibench_real.q087/e1a1","step":"analysis","kind":"tool","name":"infer.ols","version":"0.1.0","code_version":"2925aeab2262","inputs":{"dataset":"urdinez_china","y":"total_china_ln","x":["str_partnership"],"controls":["democracy","taiwan","agree_us","chinese_exports_log","total_deaths_log","gdp_per_capita_log"],"fe":[],"cluster":null,"se":"HC1","weights":null,"subset":null,"intercept":true,"level":0.95},"input_digest":"989af855e2ca6dff7af48e182c5c55700774b98b5356c64a9cd01f7b2c550cd2","dataset_digests":{"urdinez_china":"1ad6a4f8dd4dac236433e58763ee29509e326480601d43cae0c5921dcf42e743"},"result":{"tool":"infer.ols","version":"0.1.0","call_id":"call_555e513e","status":"ok","reason":"","figures":[{"key":"coef.str_partnership","value":0.4798602909914369,"unit":"","label":"Coefficient on str_partnership in the regression of total_china_ln","se":0.04112962772367135,"ci_low":0.3988647890239522,"ci_high":0.5608557929589215,"ci_level":0.95,"derivation":{"steps":[{"text_pt":"Coeficiente de str_partnership na regressão por mínimos quadrados ordinários de total_china_ln.","formula":"","terms":[{"symbol":"n","value":264.0,"unit":"observações","note":""},{"symbol":"k","value":8.0,"unit":"parâmetros","note":""}],"result":0.4798602909914369,"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":0.0016916462766877946,"unit":"","note":"diagonal da matriz de covariância"}],"result":0.04112962772367135,"unit":""},{"text_pt":"Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 256 graus de liberdade vezes o erro-padrão.","formula":"IC = β ± c · ep","terms":[{"symbol":"β","value":0.4798602909914369,"unit":"","note":""},{"symbol":"c","value":1.9692738896556876,"unit":"","note":"quantil 0.975"},{"symbol":"ep","value":0.04112962772367135,"unit":"","note":""}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"n_obs","value":264,"flag":false,"note":"observations used"},{"name":"k","value":8,"flag":false,"note":"parameters estimated, fixed-effect dummies included"},{"name":"r2","value":0.8032331306426179,"flag":false,"note":""},{"name":"df_resid","value":256,"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":0.5977959938208326,"se":0.4496490182566915,"t":1.3294724764184147,"p":0.1848756210394685,"ci_low":-0.28768607734138363,"ci_high":1.4832780649830488},{"term":"str_partnership","coef":0.4798602909914369,"se":0.04112962772367135,"t":11.667022473808164,"p":1.6641866086638344e-25,"ci_low":0.3988647890239522,"ci_high":0.5608557929589215},{"term":"democracy","coef":-0.6416228965932252,"se":0.08240219859909138,"t":-7.786477879247996,"p":1.7126788999431735e-13,"ci_low":-0.8038953947446383,"ci_high":-0.4793503984418121},{"term":"taiwan","coef":-0.738187424481077,"se":0.05272233580815547,"t":-14.001417296213361,"p":1.8567620111987278e-33,"ci_low":-0.8420121437897367,"ci_high":-0.6343627051724173},{"term":"agree_us","coef":2.0436892356955267,"se":0.5075048295098399,"t":4.026935542011236,"p":0.00007450758329668867,"ci_low":1.0442732260676377,"ci_high":3.0431052453234155},{"term":"chinese_exports_log","coef":-0.03496105836703658,"se":0.00628372912314692,"t":-5.563743707260239,"p":6.64478429095237e-8,"ci_low":-0.047335442058918833,"ci_high":-0.022586674675154322},{"term":"total_deaths_log","coef":0.09894069678699977,"se":0.019221693739421588,"t":5.147345396731779,"p":5.26802646688378e-7,"ci_low":0.06108791719099864,"ci_high":0.13679347638300088},{"term":"gdp_per_capita_log","coef":0.10437577873189129,"se":0.043389271678177924,"t":2.4055665074550157,"p":0.01685728632392524,"ci_low":0.018930418924878484,"ci_high":0.1898211385389041}]},"datasets":{},"warnings":["33 rows were dropped because one of 'total_china_ln', 'str_partnership', 'democracy', 'taiwan', 'agree_us', 'chinese_exports_log', 'total_deaths_log', 'gdp_per_capita_log' 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":"88303fe4760445dca9ebbc1be3c400cd63e4d17928d9f9ddf765b233c1fc8d89","params_versions":[],"sources":[],"started_at":"2026-10-15T17:11:01.345379Z","duration_ms":7.127665914595127,"error":null,"decided_by":"model","rationale":"causcibench_metadata_rule/1: ols","cached":false,"informed_by":[],"model":null,"prompt":null,"response":null,"tokens_in":null,"tokens_out":null,"meta":{"turn_id":"call_c116708c","llm_name":"infer__ols","vendor_id":"script_1","result_for_model_ref":"03bcdbbb4aa339ed1c3fffbcc60eed8306b48bafd1f939f144036b2e83b399f9"}}
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