{"call_id":"call_aab7d351","run_id":"20261015T171059Z-suite-causcibench_real-scripted-ded01d/causcibench_real.q026/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":"2026-10-15T17:11:00.255949Z","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\": \"thomas_effects_of\",\n \"rows\": 687,\n \"description\": \"The dataset was collected to better understand the impacts of rural electrification at the household level, particularly in regions where electricity access was expanding but remained incomplete. The data comes from a household survey conducted across 686 households in 120 habitations in Uttar Pradesh, India. According to state regulations, households must be located within 40 meters of a power pole to be eligible for a legal electricity connection. Using this rule, the study sampled households situated 20-35 meters from the nearest pole, which were eligible to get electricity from the given pole, and 45-60 meters from the pole, those that were ineligible. Houses in the 35-45 meter range are excluded to minimize measurement error. The survey targeted areas with a balanced mix of electrified and non-electrified households and collected detailed information on household demographics, expenditures, appliance ownership and use, and daily activities.\\n\\nThe variables are:\\nfood_expenditure: total monthly household expenditure on food in rupees\\neducation_expenditure: total monthly household expenditure on education in rupees\\nkerosene_expenditure: total monthly household expenditure on kerosene in rupees\\ntotal_expenditure: total monthly household expenditure in rupees\\nage: age of the head of the household\\nreligion: 1 = Hindu, 0 = otherwise\\ndistance: distance of the household from the electric grid\\ntreat: 1 if the household is actually connected to the grid, 0 if not connected to the grid\\nforcing: 1 if the household is eligible to get connected to the grid (within 40 meters), 0 if the household is not eligible to get connected to the grid\\nkerosene_lamps: 1 = household has kerosene lamp, 0 = no kerosene lamp\\nnum_kerosene_lamps: number of kerosene wick lamps and lanterns owned by the household\\nkerosene_lamp_hours: number of hours kerosene lamps are used daily\\nkerosene_other: liters of kerosene used for other household purposes\\nlighting_hours: total daily hours of household light usage\\nchild_lighting: daily hours of lighting used by children for reading and studying\\nadult_lighting: daily hours of lighting used by adults for reading and studying\\nchild_activity: number of hours children spend at home in a given day\\nadult_activity: number of hours adults spend at home in a given day\\nappliances: number of appliances owned by the household\\nappliance_use: number of daily hours using appliances by the household\\nsatisfaction_reliability: satisfaction with the reliability of lighting\\nsatisfaction_cost: satisfaction with the cost of lighting\\nsatisfaction_safety: satisfaction with the safety of lighting\\nsatisfaction_brightness: satisfaction with the brightness of lighting\\nsatisfaction: overall satisfaction with lighting\\nsatisfaction_chng: change in satisfaction with lighting over the past five years\\nelec_value: willingness to pay for adequate electricity\\nincome_increase: belief that electrification will increase household income\\nbusiness_interest: interest in starting a new business due to electrification\\nsatisfaction_business: belief that electrification supports business aspirations\\naspirations: mean value of five questions measuring general aspirations\\nknowledge: battery of questions related to knowledge of politics and popular culture\",\n \"columns\": {\n  \"total_expenditure\": \"float64\",\n  \"food_expenditure\": \"float64\",\n  \"education_expenditure\": \"float64\",\n  \"kerosene_expenditure\": \"float64\",\n  \"electricity_expenditure\": \"float64\",\n  \"kerosene_lamps\": \"float64\",\n  \"num_kerosene_lamps\": \"int64\",\n  \"kerosene_lamp_hours\": \"int64\",\n  \"kerosene_other\": \"int64\",\n  \"lighting_hours\": \"float64\",\n  \"child_lighting\": \"float64\",\n  \"adult_lighting\": \"float64\",\n  \"appliances\": \"int64\",\n  \"appliance_use\": \"float64\",\n  \"adult_activity\": \"float64\",\n  \"child_activity\": \"float64\",\n  \"satisfaction_reliability\": \"float64\",\n  \"satisfaction_cost\": \"float64\",\n  \"satisfaction_safety\": \"float64\",\n  \"satisfaction_brightness\": \"float64\",\n  \"satisfaction\": \"float64\",\n  \"satisfaction_chng\": \"float64\",\n  \"elec_value\": \"float64\",\n  \"satisfaction_business\": \"float64\",\n  \"income_increase\": \"float64\",\n  \"business_interest\": \"float64\",\n  \"aspirations\": \"float64\",\n  \"knowledge\": \"float64\",\n  \"fuel_type\": \"float64\",\n  \"distance\": \"float64\",\n  \"forcing\": \"float64\",\n  \"treat\": \"float64\",\n  \"katiya\": \"float64\",\n  \"gender\": \"float64\",\n  \"birthplace\": \"float64\",\n  \"age\": \"float64\",\n  \"religion\": \"float64\",\n  \"caste\": \"float64\",\n  \"education\": \"object\",\n  \"homeBuilt\": \"float64\",\n  \"yrsConnected\": \"float64\",\n  \"elec_hours\": \"float64\",\n  \"Pole\": \"float64\",\n  \"hours_avail\": \"float64\",\n  \"outages\": \"float64\",\n  \"low_voltage\": \"float64\",\n  \"adult_home_work\": \"float64\",\n  \"adult_home_cook\": \"float64\",\n  \"adult_home_charge\": \"float64\",\n  \"adult_home_clean\": \"float64\",\n  \"adult_home_leisure\": \"float64\",\n  \"adult_home_time\": \"float64\",\n  \"hb_r\": \"float64\",\n  \"yrs_r\": \"float64\",\n  \"el_r\": \"float64\",\n  \"ex_zsc\": \"float64\",\n  \"ex_icw\": \"float64\",\n  \"k_zsc\": \"float64\",\n  \"k_icw\": \"float64\",\n  \"l_zsc\": \"float64\",\n  \"l_icw\": \"float64\",\n  \"a_zsc\": \"float64\",\n  \"a_icw\": \"float64\",\n  \"ha_zsc\": \"float64\",\n  \"ha_icw\": \"float64\",\n  \"sa_zsc\": \"float64\",\n  \"sa_icw\": \"float64\",\n  \"village\": \"str\",\n  \"te_log\": \"float64\",\n  \"fe_log\": \"float64\",\n  \"lighting_business\": \"float64\",\n  \"lighting_friends_family\": \"float64\",\n  \"lighting_studying\": \"float64\",\n  \"lighting_reading\": \"float64\",\n  \"lighting_media\": \"float64\",\n  \"lighting_other\": \"float64\",\n  \"mobile\": \"float64\",\n  \"ic_bulb\": \"float64\",\n  \"cfl_bulb\": \"float64\",\n  \"led_light\": \"float64\",\n  \"tube_light\": \"float64\",\n  \"fans\": \"float64\",\n  \"iron\": \"float64\",\n  \"refrigerator\": \"float64\",\n  \"television\": \"float64\",\n  \"radio\": \"float64\",\n  \"cooler\": \"float64\",\n  \"washing_machine\": \"float64\",\n  \"electric_stove\": \"float64\",\n  \"inverter\": \"float64\",\n  \"water_pump\": \"float64\",\n  \"other\": \"float64\"\n }\n}\n\n# Question\nWhat is the effect of electrification on education expenses?","response":"causcibench_metadata_rule/1: iv\ninfer__iv_2sls({\"dataset\": \"thomas_effects_of\", \"y\": \"education_expenditure\", \"endog\": [\"treat\"], \"instruments\": [\"forcing\"], \"exog\": [\"gender\", \"birthplace\", \"age\", \"religion\", \"caste\", \"Pole\"]})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_aab7d351","text":"causcibench_metadata_rule/1: iv","tool_calls":[{"vendor_id":"script_1","llm_name":"infer__iv_2sls","tool_id":"infer.iv_2sls","args":{"dataset":"thomas_effects_of","y":"education_expenditure","endog":["treat"],"instruments":["forcing"],"exog":["gender","birthplace","age","religion","caste","Pole"]}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"34fde1a8a3743be96e3505b94ce86f3f2251469d659f1c5b22e05117b008c5cc","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["b121b1d603bec6f599efce242478ddedae5cbea5f951aa5e036e5bb974929c4f"],"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":0.8929159957915545}}
{"call_id":"call_6de0c9d3","run_id":"20261015T171059Z-suite-causcibench_real-scripted-ded01d/causcibench_real.q026/e1a1","step":"analysis","kind":"tool","name":"infer.iv_2sls","version":"0.1.0","code_version":"2925aeab2262","inputs":{"dataset":"thomas_effects_of","y":"education_expenditure","endog":["treat"],"instruments":["forcing"],"exog":["gender","birthplace","age","religion","caste","Pole"],"cluster":null,"se":"HC1","subset":null,"level":0.95},"input_digest":"8e4c82317de74cb8d5a0a104de091b3a2de274cfc687dfd33aaccb4a4731fa79","dataset_digests":{"thomas_effects_of":"6738107f98a584269eea39939b4755fca0f62827d7ae58d618a4490ed8f66eab"},"result":{"tool":"infer.iv_2sls","version":"0.1.0","call_id":"call_6de0c9d3","status":"ok","reason":"","figures":[{"key":"coef.treat","value":599.6371283986874,"unit":"","label":"2SLS effect of treat on education_expenditure","se":168.22161687560248,"ci_low":269.33918878492824,"ci_high":929.9350680124467,"ci_level":0.95,"derivation":{"steps":[{"text_pt":"Com um instrumento e um regressor endógeno, o MQ2E é a razão entre o coeficiente de forcing na forma reduzida (education_expenditure sobre forcing e os controles) e no primeiro estágio (treat sobre forcing e os controles).","formula":"β = ρ / π","terms":[{"symbol":"ρ","value":347.8181945039872,"unit":"","note":"forma reduzida"},{"symbol":"π","value":0.5800477956273739,"unit":"","note":"primeiro estágio"}],"result":599.6371283986874,"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":28298.512384241985,"unit":"","note":"diagonal da matriz de covariância"}],"result":168.22161687560248,"unit":""},{"text_pt":"Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 678 graus de liberdade vezes o erro-padrão.","formula":"IC = β ± c · ep","terms":[{"symbol":"β","value":599.6371283986874,"unit":"","note":""},{"symbol":"c","value":1.9634690579511542,"unit":"","note":"quantil 0.975"},{"symbol":"ep","value":168.22161687560248,"unit":"","note":""}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"first_stage_F.treat","value":444.84285482411696,"flag":false,"note":"Wald F of the excluded instruments in the first stage for treat (heteroskedasticity-robust standard errors (HC1))"},{"name":"partial_r2.treat","value":0.3945803744747516,"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":"n_obs","value":686,"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":227.16790970122125,"se":1437.8052699269454,"t":0.15799629786637492,"p":0.8745067598333252,"ci_low":-2595.9182491594433,"ci_high":3050.2540685618856},{"term":"gender","coef":-376.6941024649905,"se":238.8808343235301,"t":-1.5769122019844084,"p":0.11528217326139979,"ci_low":-845.7292291967979,"ci_high":92.34102426681687},{"term":"birthplace","coef":-334.59584105146644,"se":251.1241672702013,"t":-1.3323920381245202,"p":0.18317903582924297,"ci_low":-827.6703731902567,"ci_high":158.4786910873238},{"term":"age","coef":-1.0385546449561056,"se":4.366660618127264,"t":-0.23783727103607885,"p":0.8120791654008386,"ci_low":-9.61235765522285,"ci_high":7.535248365310638},{"term":"religion","coef":87.15614263246083,"se":93.6329035542302,"t":0.9308281525412895,"p":0.3522737761065272,"ci_low":-96.68916630239481,"ci_high":271.00145156731645},{"term":"caste","coef":-38.28181987807789,"se":98.0539579493109,"t":-0.3904158555013936,"p":0.6963516964846549,"ci_low":-230.80773232119347,"ci_high":154.24409256503768},{"term":"Pole","coef":0.2430133185657275,"se":1.3008944124162856,"t":0.18680479848810616,"p":0.8518695669319573,"ci_low":-2.311252607875197,"ci_high":2.797279245006652},{"term":"treat","coef":599.6371283986874,"se":168.22161687560248,"t":3.564566430496924,"p":0.00039004809435620513,"ci_low":269.33918878492824,"ci_high":929.9350680124467}],"first_stage":[{"endog":"treat","instrument":"forcing","coef":0.5800477956273739,"se":0.0275017589408641,"t":21.09129808295632,"p":2.600071637049728e-76,"ci_low":0.5260489429077557,"ci_high":0.634046648346992}],"reduced_form":[{"instrument":"forcing","coef":347.8181945039872,"se":97.60145456368792,"t":3.5636578989406886,"p":0.00039137613926125615,"ci_low":156.18075845716047,"ci_high":539.4556305508139}]},"datasets":{},"warnings":["1 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