{"call_id":"call_4c924f24","run_id":"20261015T171142Z-suite-causcibench_real-naive-d6494d/causcibench_real.q117/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":"2026-10-15T17:11:44.937120Z","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\": \"newman_global_costs\",\n \"rows\": 191,\n \"description\": \"The study examines the global economic costs of extreme weather events attributable to climate change using Event Attribution methodology. It analyzes 185 extreme weather events from 2000-2019 across multiple countries, combining data from climate attribution studies with socio-economic impact data from the Emergency Management Database (EM-DAT). The methodology uses climate models to compare observed extreme weather events with counterfactual scenarios without anthropogenic greenhouse gas emissions. This generates Fraction of Attributable Risk (FAR) estimates quantifying what portion of each event's risk is attributable to human-induced climate change. These FAR values are then applied to observed mortality and economic damages to calculate climate change-attributable costs globally.\\n\\nVariable description:\\n- Event Identifier: Identifies the specific extreme weather event\\n- Year: Year of event\\n- Country/region: Country of event\\n- Continent: Continent where event happened\\n- Disaster Type: One of Flood, Heatwave, Wildfire, Drought, Storm, Cold wave\\n- FAR Best Estimate: FAR value = 1 - p_1/p_0, where p_1 is the probability of event in the real world, and p_0 in counterfactual world without climate change.\\n- Total Deaths: Total number of deaths caused by event\\n- Total Affected\\n- Total Damage (US$ Thousands): Amount of damage caused in thousands of US dollars per event\\n- Insured Loss (US$ Thousands): Amount of economic damage insured\\n- Uninsured damages: Difference of previous two\\n- Total Damage (US$ Millions): Amount of damage caused in millions of US dollars per event\\n- Insured Loss (US$ Millions): Amount of economic damage insured (in millions)\",\n \"columns\": {\n  \"Event Identifier\": \"str\",\n  \"Year\": \"float64\",\n  \"Country/region\": \"str\",\n  \"Continent\": \"str\",\n  \"Disaster Type\": \"str\",\n  \"FAR Best Estimate\": \"float64\",\n  \"Total Deaths\": \"float64\",\n  \"Total Affected\": \"float64\",\n  \"Total Damage (US$ Thousands)\": \"float64\",\n  \"Insured Loss (US$ Thousands)\": \"float64\",\n  \"Uninsured damages\": \"float64\",\n  \"Total Damage (US$ Millions)\": \"float64\",\n  \"Insured Losses (US$ Millions)\": \"float64\",\n  \"Attributed Damage (US$ Thousands)\": \"float64\",\n  \"Attributed Deaths\": \"float64\"\n }\n}\n\n# Question\nHow many global deaths due to extreme weather are attributable to climate change?","response":"Linha de base ingênua: regressão do resultado no tratamento, sem controles.\ninfer__ols({\"dataset\": \"newman_global_costs\", \"y\": \"Total Deaths\", \"x\": [\"FAR Best Estimate\"]})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_4c924f24","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":"newman_global_costs","y":"Total Deaths","x":["FAR Best Estimate"]}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"1b50e0c1972f1e29dac18bf4ff5c2d8cc3f72e550fb050517e95a73dcda6083b","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["394b15bc1b4845a3e4b2e09e7ba63312ccb5e4310756a142ad7b3a5079654afb"],"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":0.7371250540018082}}
{"call_id":"call_cb7066eb","run_id":"20261015T171142Z-suite-causcibench_real-naive-d6494d/causcibench_real.q117/e1a1","step":"analysis","kind":"tool","name":"infer.ols","version":"0.1.0","code_version":"2925aeab2262","inputs":{"dataset":"newman_global_costs","y":"Total Deaths","x":["FAR Best Estimate"],"controls":[],"fe":[],"cluster":null,"se":"HC1","weights":null,"subset":null,"intercept":true,"level":0.95},"input_digest":"53fce5afc8f9d99b8a68da3c06710e3922e3ab48a187ac65fe418d95aadcb625","dataset_digests":{"newman_global_costs":"6d5cad51d45205e9a2becb995681807ee23c75f78a49e411ace6a31ff6cc0d24"},"result":{"tool":"infer.ols","version":"0.1.0","call_id":"call_cb7066eb","status":"ok","reason":"","figures":[{"key":"coef.FAR Best Estimate","value":-205.11810799915094,"unit":"","label":"Coefficient on FAR Best Estimate in the regression of Total Deaths","se":1163.325221237423,"ci_low":-2510.0979128937233,"ci_high":2099.861696895421,"ci_level":0.95,"derivation":{"steps":[{"text_pt":"Coeficiente de FAR Best Estimate na regressão por mínimos quadrados ordinários de Total Deaths.","formula":"","terms":[{"symbol":"n","value":114.0,"unit":"observações","note":""},{"symbol":"k","value":2.0,"unit":"parâmetros","note":""}],"result":-205.11810799915094,"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":1353325.570367099,"unit":"","note":"diagonal da matriz de covariância"}],"result":1163.325221237423,"unit":""},{"text_pt":"Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 112 graus de liberdade vezes o erro-padrão.","formula":"IC = β ± c · ep","terms":[{"symbol":"β","value":-205.11810799915094,"unit":"","note":""},{"symbol":"c","value":1.9813718148763053,"unit":"","note":"quantil 0.975"},{"symbol":"ep","value":1163.325221237423,"unit":"","note":""}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"n_obs","value":114,"flag":false,"note":"observations used"},{"name":"k","value":2,"flag":false,"note":"parameters estimated, fixed-effect dummies included"},{"name":"r2","value":0.00027103240970849374,"flag":false,"note":""},{"name":"df_resid","value":112,"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":1416.1640891859975,"se":726.2021820606811,"t":1.9500961635332341,"p":0.05366378249916706,"ci_low":-22.712446250707444,"ci_high":2855.040624622702},{"term":"FAR Best Estimate","coef":-205.11810799915094,"se":1163.325221237423,"t":-0.1763205200528257,"p":0.8603605683368692,"ci_low":-2510.0979128937233,"ci_high":2099.861696895421}]},"datasets":{},"warnings":["77 rows were dropped because one of 'Total Deaths', 'FAR Best Estimate' 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":"55ea1a77e9ce2f9c4b9e3dd05ddab39c4cea71238f8ed1f2b8cea663a430e8af","params_versions":[],"sources":[],"started_at":"2026-10-15T17:11:44.939535Z","duration_ms":11.7037920281291,"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_4c924f24","llm_name":"infer__ols","vendor_id":"script_1","result_for_model_ref":"8f168ab828930d87d63d489e19dc60285fe73972856ef8a3e491882069e66661"}}
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