{"call_id":"call_9b58f99d","run_id":"20261015T171059Z-suite-causcibench_real-scripted-ded01d/causcibench_real.q069/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":"2026-10-15T17:11:00.872156Z","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\": \"goldsmith_doing_well\",\n \"rows\": 153,\n \"description\": \"The dataset combines multinational survey data on public opinion with country-level information on aid flows, health indicators, governance, and economic conditions. Public opinion measures come from the Gallup World Poll (GWP), an annual global survey launched in 2006 that samples individuals aged 15 and older. In countries with high telephone penetration (over 80%), data are collected through Random-Digit-Dial (RDD) telephone interviews; elsewhere, surveys are conducted face-to-face. Each survey includes about 1,000 respondents per country per year. Aid-related data include figures from the U.S. President's Emergency Plan for AIDS Relief (PEPFAR), one of the largest U.S. bilateral health initiatives, launched in 2003 and first disbursed in 2004, aimed at combating HIV/AIDS in dozens of countries, especially in Sub-Saharan Africa. The dataset also incorporates U.S. Official Development Assistance (ODA) compiled from international aid databases. Additional country-level indicators are drawn from international sources: population and economic statistics from the World Bank's World Development Indicators (WDI), health data from UNAIDS and global monitoring agencies, governance measures from the World Bank Governance Indicators, civil liberties scores from Freedom House, UN General Assembly voting data, and trade flows from the U.S. Department of Commerce.\\n\\nData variables:\\ncountry: Country name\\nccode: Numeric country code\\ny_us: Net approval of the U.S. (log(%approve) - log(%disapprove))\\ny_ch: Net approval of China\\ny_fr: Net approval of France\\ny_ge: Net approval of Germany\\ny_in: Net approval of India\\ny_ja: Net approval of Japan\\ny_ru: Net approval of Russia\\ny_uk: Net approval of the U.K.\\ny_ownc: Net approval of respondent's own country\\nx: PEPFAR funds per capita (log)\\nx_imputed: PEPFAR funds per capita (log, imputed)\\nz_ratio: HIV prevalence rate (log)\\nz_death: HIV-related deaths per capita (log)\\nusaid: U.S. ODA per capita (log)\\nusaid_imputed: U.S. ODA per capita (log, imputed)\\ngdppc: GDP per capita (log)\\nwbgi: World Bank governance indicator\\nfhscore: Freedom House civil liberties score\\nafrica: 1 if African country, 0 otherwise\\ntrade_i: Imports to the U.S. (log)\\ntrade_e: Exports to the U.S. (log)\\nunvote_key: UN voting alignment with the U.S.\",\n \"columns\": {\n  \"Unnamed: 0\": \"int64\",\n  \"country\": \"str\",\n  \"ccode\": \"float64\",\n  \"y_us\": \"float64\",\n  \"y_ch\": \"float64\",\n  \"y_fr\": \"float64\",\n  \"y_ge\": \"float64\",\n  \"y_in\": \"float64\",\n  \"y_ja\": \"float64\",\n  \"y_ru\": \"float64\",\n  \"y_uk\": \"float64\",\n  \"y_ownc\": \"float64\",\n  \"x\": \"float64\",\n  \"x_imputed\": \"float64\",\n  \"z_ratio\": \"float64\",\n  \"z_death\": \"float64\",\n  \"usaid\": \"float64\",\n  \"usaid_imputed\": \"float64\",\n  \"gdppc\": \"float64\",\n  \"wbgi\": \"float64\",\n  \"fhscore\": \"float64\",\n  \"africa\": \"float64\",\n  \"trade_i\": \"float64\",\n  \"trade_e\": \"float64\",\n  \"unvote_key\": \"float64\"\n }\n}\n\n# Question\nDoes PEPFAR funding increase approval of U.S. leadership?","response":"causcibench_metadata_rule/1: iv\ninfer__iv_2sls({\"dataset\": \"goldsmith_doing_well\", \"y\": \"y_us\", \"endog\": [\"x\"], \"instruments\": [\"z_ratio\", \"z_death\"], \"exog\": [\"usaid\", \"gdppc\", \"wbgi\", \"fhscore\", \"africa\", \"trade_i\", \"trade_e\", \"unvote_key\"]})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_9b58f99d","text":"causcibench_metadata_rule/1: iv","tool_calls":[{"vendor_id":"script_1","llm_name":"infer__iv_2sls","tool_id":"infer.iv_2sls","args":{"dataset":"goldsmith_doing_well","y":"y_us","endog":["x"],"instruments":["z_ratio","z_death"],"exog":["usaid","gdppc","wbgi","fhscore","africa","trade_i","trade_e","unvote_key"]}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"d8b4542074b9c99db1e9a43587fdebf1de02f5b84eeedf75b5d92bc63fb3b024","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["3ae488df7c02b554dfe0c81d560e99195bfcbbce3742c0cdfff4d9a48a9c41a4"],"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":0.9072921238839626}}
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resíduos com os valores observados de x.","formula":"","terms":[{"symbol":"n","value":57.0,"unit":"observações","note":""},{"symbol":"instrumentos","value":2.0,"unit":"","note":""}],"result":0.2016168028931283,"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":0.00568432785134805,"unit":"","note":"diagonal da matriz de covariância"}],"result":0.07539448157092168,"unit":""},{"text_pt":"Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 47 graus de liberdade vezes o erro-padrão.","formula":"IC = β ± c · ep","terms":[{"symbol":"β","value":0.2016168028931283,"unit":"","note":""},{"symbol":"c","value":2.0117405137297655,"unit":"","note":"quantil 0.975"},{"symbol":"ep","value":0.07539448157092168,"unit":"","note":""}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"first_stage_F.x","value":25.508852768251494,"flag":false,"note":"Wald F of the excluded instruments in the first stage for x (heteroskedasticity-robust standard errors (HC1))"},{"name":"partial_r2.x","value":0.45145917151222803,"flag":false,"note":"share of the variation the instruments explain beyond the controls"},{"name":"weak_instrument","value":false,"flag":false,"note":"every first-stage F is at least 10"},{"name":"overid_p","value":0.3457975250962223,"flag":false,"note":"Sargan test of the overidentifying restrictions (assumes homoskedastic errors)"},{"name":"sargan","value":0.8888190860442153,"flag":false,"note":"n·R² of the structural residuals on the instruments"},{"name":"sargan_df","value":1,"flag":false,"note":""},{"name":"n_obs","value":57,"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":4.089382493202767,"se":1.8930767509615973,"t":2.1601778644872933,"p":0.035890660011518125,"ci_low":0.2810032976934078,"ci_high":7.8977616887121265},{"term":"usaid","coef":-0.10683577630707236,"se":0.07396466269337133,"t":-1.4444164607357415,"p":0.1552557603276624,"ci_low":-0.255633484831684,"ci_high":0.0419619322175393},{"term":"gdppc","coef":-0.3051988030274777,"se":0.20376702605005767,"t":-1.4977830758176849,"p":0.1408761453518549,"ci_low":-0.7151251846946072,"ci_high":0.10472757863965182},{"term":"wbgi","coef":-0.5275336786956998,"se":0.4562374523074205,"t":-1.1562699993779526,"p":0.2534144322784223,"ci_low":-1.4453650453833893,"ci_high":0.3902976879919897},{"term":"fhscore","coef":-0.2503209096889133,"se":0.16606580802332532,"t":-1.5073597188275725,"p":0.13841118107678746,"ci_low":-0.5844022236347064,"ci_high":0.0837604042568798},{"term":"africa","coef":0.8037341085325941,"se":0.3497096407356625,"t":2.2982898236429183,"p":0.0260402996208304,"ci_low":0.10020905622278065,"ci_high":1.5072591608424075},{"term":"trade_i","coef":0.08041864479105566,"se":0.0977554896374261,"t":0.8226509333575784,"p":0.4148608857009515,"ci_low":-0.11624003415204469,"ci_high":0.277077323734156},{"term":"trade_e","coef":-0.05167948682217305,"se":0.1432079273108187,"t":-0.3608702939328758,"p":0.7198134085002114,"ci_low":-0.33977667608061435,"ci_high":0.23641770243626825},{"term":"unvote_key","coef":0.9702157089535838,"se":0.7636376453402579,"t":1.2705184387829385,"p":0.21015332447655968,"ci_low":-0.5660250799866152,"ci_high":2.5064564978937827},{"term":"x","coef":0.2016168028931283,"se":0.07539448157092168,"t":2.674158621323929,"p":0.010272245995103067,"ci_low":0.04994266980525297,"ci_high":0.35329093598100364}],"first_stage":[{"endog":"x","instrument":"z_ratio","coef":1.0306491036053678,"se":0.34465235419304974,"t":2.9904020415542307,"p":0.00446407948881963,"ci_low":0.3368998966929577,"ci_high":1.7243983105177778},{"endog":"x","instrument":"z_death","coef":0.01178041258773006,"se":0.24170974060651668,"t":0.04873784795833938,"p":0.9613392159101395,"ci_low":-0.4747560604950842,"ci_high":0.4983168856705443}],"reduced_form":[{"instrument":"z_ratio","coef":0.039782974361053755,"se":0.2051740271837986,"t":0.1938986864327396,"p":0.8471089345405091,"ci_low":-0.3732109219697897,"ci_high":0.45277687069189726},{"instrument":"z_death","coef":0.1303729466694539,"se":0.15948086910367892,"t":0.8174832969131747,"p":0.41786647218140255,"ci_low":-0.19064539286118695,"ci_high":0.4513912862000947}]},"datasets":{},"warnings":["96 rows were dropped because one of 'y_us', 'x', 'z_ratio', 'z_death', 'usaid', 'gdppc', 'wbgi', 'fhscore', 'africa', 'trade_i', 'trade_e', 'unvote_key' was missing"],"assumptions":["the instruments affect the outcome only through the endogenous regressors (exclusion restriction)","the instruments are as good as randomly assigned given the controls","errors are independent across observations; 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