{"call_id":"call_543da950","run_id":"20261015T171059Z-suite-causcibench_real-scripted-ded01d/causcibench_real.q021/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":"2026-10-15T17:11:00.134632Z","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\": \"thornton_the_demand\",\n \"rows\": 2812,\n \"description\": \"This dataset comes from a randomized field experiment conducted in rural Malawi between May and August 2004, as part of the Malawi Diffusion and Ideational Change Project (MDICP). The MDICP is a long-term study involving approximately 120 villages across three districts: Rumphi, Mchinji, and Balaka. As part of the program, respondents were offered a free HIV test administered at home. To encourage participants to collect their test results, they were randomly given vouchers of varying value, redeemable at nearby voluntary counseling and testing (VCT) centers. Voucher amounts were determined by drawing tokens from a bag. Researchers then recorded whether participants retrieved their results from the assigned VCT center.\\n\\nVariables:\\nsite: Region of Malawi where the study was conducted (1 = Mchinji, 2 = Balaka, 3 = Rumphi)\\nrumphi: Indicator for living in Rumphi district\\nbalaka: Indicator for living in Balaka district\\nvillnum: Village ID\\nsurvey2004: Indicator for completing the 2004 baseline survey\\ngot: Indicator for whether the respondent obtained their HIV test result\\nzone: Identifier for the VCT center assigned to the respondent\\ndistvct: Distance (in kilometers) to the assigned VCT center\\ntinc: Total incentive value in Malawi Kwacha\\nTi: Discrete incentive level category (e.g., 0; 10-50; 60-100; 110-200; 210-300 Malawi Kwacha)\\nany: Indicator for receiving a positive-valued incentive\\nunder: Indicator for living within 1.5 km of the assigned VCT center\\nover: Indicator for living more than 1.5 km from the assigned VCT center\\nsimaverage: Simulated average distance to a VCT center (based on randomly drawn household locations)\\nage: Respondent's age in 2004\\nmale: Indicator for male respondents\\nhiv2004: HIV test result in 2004 (-1 indicates indeterminate result)\",\n \"columns\": {\n  \"got\": \"float64\",\n  \"any\": \"float64\",\n  \"tinc\": \"float64\",\n  \"male\": \"float64\",\n  \"hiv2004\": \"float64\",\n  \"age\": \"float64\",\n  \"rumphi\": \"float64\",\n  \"balaka\": \"float64\",\n  \"over\": \"float64\",\n  \"under\": \"float64\",\n  \"age2\": \"float64\",\n  \"villnum\": \"float64\"\n }\n}\n\n# Question\nDoes providing monetary incentives lead individuals to obtain their HIV test results?","response":"causcibench_metadata_rule/1: ols\ninfer__ols({\"dataset\": \"thornton_the_demand\", \"y\": \"got\", \"x\": [\"any\"], \"controls\": [\"male\", \"hiv2004\", \"age\", \"rumphi\", \"balaka\"]})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_543da950","text":"causcibench_metadata_rule/1: ols","tool_calls":[{"vendor_id":"script_1","llm_name":"infer__ols","tool_id":"infer.ols","args":{"dataset":"thornton_the_demand","y":"got","x":["any"],"controls":["male","hiv2004","age","rumphi","balaka"]}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"e5e510d9838cb6f496700e24996bf7922ac1ae916751de77602299cf9178a5ed","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["0d6cfebe112870cd34daa79ba2f0c54a0642682b8189a0bcf381f0c556f114fe"],"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":0.757833942770958}}
{"call_id":"call_a5efd25d","run_id":"20261015T171059Z-suite-causcibench_real-scripted-ded01d/causcibench_real.q021/e1a1","step":"analysis","kind":"tool","name":"infer.ols","version":"0.1.0","code_version":"2925aeab2262","inputs":{"dataset":"thornton_the_demand","y":"got","x":["any"],"controls":["male","hiv2004","age","rumphi","balaka"],"fe":[],"cluster":null,"se":"HC1","weights":null,"subset":null,"intercept":true,"level":0.95},"input_digest":"88f0d5a15e8a6a13f6184871e401a263df99237486fde1a9ec7612a1164c1913","dataset_digests":{"thornton_the_demand":"a41ec4eb81c162e73afdde537962f133576fbf0197169fd41c12ff55dd05e57f"},"result":{"tool":"infer.ols","version":"0.1.0","call_id":"call_a5efd25d","status":"ok","reason":"","figures":[{"key":"coef.any","value":0.4311055091994172,"unit":"","label":"Coefficient on any in the regression of got","se":0.021484015658595184,"ci_low":0.3889794348901319,"ci_high":0.4732315835087025,"ci_level":0.95,"derivation":{"steps":[{"text_pt":"Coeficiente de any na regressão por mínimos quadrados ordinários de got.","formula":"","terms":[{"symbol":"n","value":2812.0,"unit":"observações","note":""},{"symbol":"k","value":7.0,"unit":"parâmetros","note":""}],"result":0.4311055091994172,"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":0.00046156292881876306,"unit":"","note":"diagonal da matriz de covariância"}],"result":0.021484015658595184,"unit":""},{"text_pt":"Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 2805 graus de liberdade vezes o erro-padrão.","formula":"IC = β ± c · ep","terms":[{"symbol":"β","value":0.4311055091994172,"unit":"","note":""},{"symbol":"c","value":1.9608100728800097,"unit":"","note":"quantil 0.975"},{"symbol":"ep","value":0.021484015658595184,"unit":"","note":""}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"n_obs","value":2812,"flag":false,"note":"observations used"},{"name":"k","value":7,"flag":false,"note":"parameters estimated, fixed-effect dummies included"},{"name":"r2","value":0.1810796501222579,"flag":false,"note":""},{"name":"df_resid","value":2805,"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":0.39237361630528084,"se":0.03017459753951478,"t":13.003441580006251,"p":1.3958444274160907e-37,"ci_low":0.3332069615046999,"ci_high":0.45154027110586176},{"term":"any","coef":0.4311055091994172,"se":0.021484015658595184,"t":20.066337506458822,"p":8.406235561579693e-84,"ci_low":0.3889794348901319,"ci_high":0.4732315835087025},{"term":"male","coef":-0.011635457063582306,"se":0.015831010427616397,"t":-0.7349788010552276,"p":0.46241384680645004,"ci_low":-0.042677061773921006,"ci_high":0.019406147646756397},{"term":"hiv2004","coef":-0.05050671246750382,"se":0.034256616431770213,"t":-1.4743637208916807,"p":0.14049590640747964,"ci_low":-0.11767743102970571,"ci_high":0.016664006094698064},{"term":"age","coef":0.001927876815358985,"se":0.0005835329545537755,"t":3.3038010969461387,"p":0.0009658160476220703,"ci_low":0.0007836795202125089,"ci_high":0.003072074110505461},{"term":"rumphi","coef":-0.13804392953953526,"se":0.02039000051165227,"t":-6.77017783597638,"p":1.5604618997909583e-11,"ci_low":-0.17802484792881157,"ci_high":-0.09806301115025894},{"term":"balaka","coef":-0.11987612608319605,"se":0.018877197071279547,"t":-6.3503138538284345,"p":2.4980730889322216e-10,"ci_low":-0.156890724248302,"ci_high":-0.0828615279180901}]},"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":"17657944f873826813efa06aacd99ce3cd9359270c17e07dad2375b8bf6a0660","params_versions":[],"sources":[],"started_at":"2026-10-15T17:11:00.137235Z","duration_ms":20.10324993170798,"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_543da950","llm_name":"infer__ols","vendor_id":"script_1","result_for_model_ref":"621481ac4603873f89aa51b5ebfd76030c2039c17aee4055f9c1a89259778154"}}
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