{"call_id":"call_416922bf","run_id":"20261015T171142Z-suite-causcibench_real-naive-d6494d/causcibench_real.q089/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":"2026-10-15T17:11:44.447151Z","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\": \"dorussen_the_influence\",\n \"rows\": 6726,\n \"description\": \"This dataset contains individual-level survey responses from adults in England merged with local NHS performance measures on emergency and cancer referral waits covering July 2022 to July 2023. Each row corresponds to a respondent observation with institutional trust ratings, demographics, constituency-level context, and area-level waiting-time indicators linked via postcode outcodes to NHS Integrated Care Boards (ICBs) and ICB sub-units.\\nVariable descriptions:\\nid: Anonymous respondent identifier that uniquely indexes each survey record within the merged file\\ntrustGov: Self-reported trust in \\\"\\\"the Government in Westminster\\\"\\\" on a 1-7 scale where 1 = Not at all and 7 = Completely\\ntrustPM: Self-reported trust in the Prime Minister on the same 1-7 scale\\ntrustParl: Self-reported trust in the UK Parliament on the 1-7 scale\\ntrustMP: Self-reported trust in the respondent's local Member of Parliament on the 1-7 scale\\ntrustNHS: Self-reported trust in the National Health Service on the 1-7 scale\\ntrustPolice: Self-reported trust in the police on the 1-7 scale\\ntrustCourts: Self-reported trust in the courts on the 1-7 scale\\ntrustTradNews: Self-reported trust in news from traditional media on the 1-7 scale\\ntrustSocNews: Self-reported trust in news shared on social media on the 1-7 scale\\nincome: Personal annual income category with 14 brackets (under 5,000; 5,000-9,999; ...; 100,000+)\\nemergencyDelay: Proportion of patients waiting more than 4 hours from decision to admit to admission across all A&E types in the respondent's ICB\\ncancerDelay: Proportion of GP-to-specialist cancer referrals breaching the two-week wait standard in the respondent's ICB sub-unit over July 2022-June 2023\\nethnicity: Indicator for membership in an ethnic minority group, derived from survey responses/profile variables\\nlondon: Indicator for residence in the London region as defined in the nine-region classification used in the survey\\nconVote19: Indicator for whether the respondent reported voting Conservative in the 2019 UK General Election\\nwomen: Indicator for respondent's sex coded as female based on survey demographics\\nhigherEd: Indicator for completion of higher education as reported in the survey\\nregion: Categorical variable for the nine regions of England used to locate respondents\\nconShare: Conservative Party vote share (%) in the respondent's parliamentary constituency at the 2019 General Election\\nunemploymentRate: Constituency-level unemployment rate (%) matched to each respondent's parliamentary constituency\\nage: Respondent age in years (real-valued), spanning 18-98 across waves\\nW8: Respondent-level survey weight provided by the survey vendor for national representativeness across pooled monthly waves\",\n \"columns\": {\n  \"id\": \"int64\",\n  \"trustGov\": \"float64\",\n  \"trustPM\": \"int64\",\n  \"trustParl\": \"float64\",\n  \"trustMP\": \"float64\",\n  \"trustNHS\": \"int64\",\n  \"trustPolice\": \"float64\",\n  \"trustCourts\": \"float64\",\n  \"trustTradNews\": \"float64\",\n  \"trustSocNews\": \"int64\",\n  \"income\": \"float64\",\n  \"emergencyDelay\": \"float64\",\n  \"cancerDelay\": \"float64\",\n  \"ethnicity\": \"int64\",\n  \"london\": \"int64\",\n  \"conVote19\": \"int64\",\n  \"women\": \"int64\",\n  \"higherEd\": \"int64\",\n  \"region\": \"int64\",\n  \"conShare\": \"float64\",\n  \"unemploymentRate\": \"float64\",\n  \"age\": \"int64\",\n  \"W8\": \"float64\"\n }\n}\n\n# Question\nHow does the local proportion of GP‑to‑specialist cancer referrals that breach the two‑week wait standard affect individuals' reported trust in the NHS on a 1–7 scale?","response":"Linha de base ingênua: regressão do resultado no tratamento, sem controles.\ninfer__ols({\"dataset\": \"dorussen_the_influence\", \"y\": \"trustNHS\", \"x\": [\"cancerDelay\"]})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_416922bf","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":"dorussen_the_influence","y":"trustNHS","x":["cancerDelay"]}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"50e6b8dcbe90497699d67798ebf9297dc84488939e39a6a40333d123bcc8122e","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["33c940486d024f959093ae32b689c9fa822d4908807bfabe8789b4e78be698cc"],"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":0.7664579898118973}}
{"call_id":"call_2fc9014a","run_id":"20261015T171142Z-suite-causcibench_real-naive-d6494d/causcibench_real.q089/e1a1","step":"analysis","kind":"tool","name":"infer.ols","version":"0.1.0","code_version":"2925aeab2262","inputs":{"dataset":"dorussen_the_influence","y":"trustNHS","x":["cancerDelay"],"controls":[],"fe":[],"cluster":null,"se":"HC1","weights":null,"subset":null,"intercept":true,"level":0.95},"input_digest":"75e3f04beee8fb9706660316833f1603dc8e9c09562cc16cf6575f123fb5c8d2","dataset_digests":{"dorussen_the_influence":"378acc9f5ca587819e3eb096a0bb4b7fa1011f6549b89f0efef3a2e3dd31aa69"},"result":{"tool":"infer.ols","version":"0.1.0","call_id":"call_2fc9014a","status":"ok","reason":"","figures":[{"key":"coef.cancerDelay","value":0.28152482522268996,"unit":"","label":"Coefficient on cancerDelay in the regression of trustNHS","se":0.146884354360049,"ci_low":-0.0064150501483840605,"ci_high":0.569464700593764,"ci_level":0.95,"derivation":{"steps":[{"text_pt":"Coeficiente de cancerDelay na regressão por mínimos quadrados ordinários de trustNHS.","formula":"","terms":[{"symbol":"n","value":6726.0,"unit":"observações","note":""},{"symbol":"k","value":2.0,"unit":"parâmetros","note":""}],"result":0.28152482522268996,"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":0.02157501355576845,"unit":"","note":"diagonal da matriz de covariância"}],"result":0.146884354360049,"unit":""},{"text_pt":"Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 6724 graus de liberdade vezes o erro-padrão.","formula":"IC = β ± c · ep","terms":[{"symbol":"β","value":0.28152482522268996,"unit":"","note":""},{"symbol":"c","value":1.9603168535245346,"unit":"","note":"quantil 0.975"},{"symbol":"ep","value":0.146884354360049,"unit":"","note":""}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"n_obs","value":6726,"flag":false,"note":"observations used"},{"name":"k","value":2,"flag":false,"note":"parameters estimated, fixed-effect dummies included"},{"name":"r2","value":0.0005301615286864569,"flag":false,"note":""},{"name":"df_resid","value":6724,"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":4.909328527795733,"se":0.0358922253567103,"t":136.7797198141101,"p":0.0,"ci_low":4.838968393518473,"ci_high":4.979688662072992},{"term":"cancerDelay","coef":0.28152482522268996,"se":0.146884354360049,"t":1.9166426979186948,"p":0.05532568072379405,"ci_low":-0.0064150501483840605,"ci_high":0.569464700593764}]},"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":"c7a8e7b142574ee17f405603d931fbac53656701010cbac58e7ca76bd522fb24","params_versions":[],"sources":[],"started_at":"2026-10-15T17:11:44.450246Z","duration_ms":6.413208087906241,"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_416922bf","llm_name":"infer__ols","vendor_id":"script_1","result_for_model_ref":"af6cec1f724c8d70d665a6ad0ae040e5ce7e42540bd556cd62a65c99897dc315"}}
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