{"call_id":"call_594942d3","run_id":"20261015T171142Z-suite-causcibench_real-naive-d6494d/causcibench_real.q125/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":"2026-10-15T17:11:45.090483Z","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\": \"cai_effect_microinsurance\",\n \"rows\": 480,\n \"description\": \"The dataset is the result of a randomized controlled trial conducted to study the effect of insurance on farming behavior. Each village consists of at least one AHW (Animal Husbandry Worker), who is involved in tasks such as animal immunization, providing technical assistance to farmers, etc. As part of the experiment, AHWs are assigned to different incentive schemes associated with the number of sow insurance purchases. In the control group villages, AHWs were offered a reward of 50 yuan to participate. In the low incentive group (LIG), the AHW is offered 20 Yuan to begin with, and an additional payment of 2 Yuan for each insured sow. Finally, in the high incentive group (HIG), the AHWs are offered a starting payment of 20 Yuan and an additional 4 Yuan for each insured sow. At the end of the experiment, data on total sows in each village were obtained.\\n\\nVariables:\\nsow0: No. of Sows in Dec. 2006\\nsow1: No. of Sows in Sept. 2007\\nsow2: No. of Sows in Dec. 2007\\npig: No. of Pigs in Dec. 2006\\ngoat: No. of Goats in Dec 2006\\npop_regist: Village Population\\npop_migout: No. of Villagers as Migrant Workers\\nage_vil: Ave. Villager Age\\nedu_vil: Ave. Villager Education (Years)\\nmale_vil: Fraction Male in Village\\nland: Land per Household (Mu)\\nhouse_val: House Value (RMB yuan)\\nlhouse_val: Log House Value\\nsurname_vil: No. of Surnames in the Village\\nno_xnh: No. of Villagers in New Medical Coop. Scheme\\nsubsidy: No. of Households Receiving Gov. Subsidy\\ninsuredpig: No. of Insured Sows\\nsnow: No. of Sow Deaths in Snow Storm\\nsow3: No. of Sows in March 2008\\nsow4: No. of Sows in June 2008\\ngroup: Coding for different groups (1 = Control Group; 2 = Low incentive Group; 3 = High Incentive Group)\\ngroupd1: Control Group Dummy\\ngroupd2: Low Incentive Group Dummy\\ngroupd3: High Incentive Dummy\\nvillageid: Village Id\\nvillagename: Village Name (in Chinese)\\nsnow_insured: No. of Sow Deaths in Snow Storm Interact with No. of Insured Sows\\ntowndummy1-26: 26 Town dummies\\nsow2008: No. of sows at the end of 2008\\nsow2009: No. of sows at the end of 2009\\nsow2010: No. of sows at the end of 2010\\nsow2011: No. of sows at the end of 2011\\npig2008: No. of pigs at the end of 2008\\npig2009: No. of pigs at the end of 2009\\npig2010: No. of pigs at the end of 2010\\npig2011: No. of pigs at the end of 2011\\nsheep2008: No. of sheep at the end of 2008\\ncow2008: No. of cows at the end of 2008\\nsow200810: Average sows 2008-2010\\npig200810: Average pigs 2008-2010\\nlnhouse: Log House Value\\ninsured_house: Insured Sows _ Log House Value\\ninsured_edu: Insured Sows _ Avg. Villager Education\\nsnow_house: Sow Deaths _ Log House Value\\nsnow_edu: Sow Deaths _ Avg. Villager Education\\ninsured_house_snow: insured_house _ snow\\ninsured_edu_snow: insured_edu _ snow\\nhouse_groupd2: Log House Value _ Low Incentive Dummy\\nhouse_groupd3: Log House Value _ High Incentive Dummy\\nhouse_snow_gd2: snow_house _ Low Incentive Dummy\\nhouse_snow_gd3: snow_house _ High Incentive Dummy\\nedu_groupd2: Avg. Villager Education _ Low Incentive Dummy\\nedu_groupd3: Avg. Villager Education _ High Incentive Dummy\\nedu_snow_gd2: insured_edu _ snow _ Low Incentive Dummy\\nedu_snow_gd3: insured_edu _ snow _ High Incentive Dummy\",\n \"columns\": {\n  \"villageid\": \"float64\",\n  \"snow\": \"float64\",\n  \"sow2\": \"float64\",\n  \"sow3\": \"float64\",\n  \"sow4\": \"float64\",\n  \"villagename\": \"str\",\n  \"sow1\": \"float64\",\n  \"group\": \"int64\",\n  \"insuredpig\": \"int64\",\n  \"pig\": \"int64\",\n  \"sow0\": \"int64\",\n  \"edu_vil\": \"float64\",\n  \"male_vil\": \"float64\",\n  \"age_vil\": \"float64\",\n  \"goat\": \"float64\",\n  \"house_val\": \"float64\",\n  \"land\": \"float64\",\n  \"subsidy\": \"float64\",\n  \"town_loc\": \"float64\",\n  \"surname_vil\": \"float64\",\n  \"pop_regist\": \"float64\",\n  \"pop_migout\": \"float64\",\n  \"no_xnh\": \"float64\",\n  \"townid\": \"float64\",\n  \"towndummy1\": \"int64\",\n  \"towndummy2\": \"int64\",\n  \"towndummy3\": \"int64\",\n  \"towndummy4\": \"int64\",\n  \"towndummy5\": \"int64\",\n  \"towndummy6\": \"int64\",\n  \"towndummy7\": \"int64\",\n  \"towndummy8\": \"int64\",\n  \"towndummy9\": \"int64\",\n  \"towndummy10\": \"int64\",\n  \"towndummy11\": \"int64\",\n  \"towndummy12\": \"int64\",\n  \"towndummy13\": \"int64\",\n  \"towndummy14\": \"int64\",\n  \"towndummy15\": \"int64\",\n  \"towndummy16\": \"int64\",\n  \"towndummy17\": \"int64\",\n  \"towndummy18\": \"int64\",\n  \"towndummy19\": \"int64\",\n  \"towndummy20\": \"int64\",\n  \"towndummy21\": \"int64\",\n  \"towndummy22\": \"int64\",\n  \"towndummy23\": \"int64\",\n  \"towndummy24\": \"int64\",\n  \"towndummy25\": \"int64\",\n  \"towndummy26\": \"int64\",\n  \"snow_insured\": \"float64\",\n  \"groupd1\": \"int64\",\n  \"groupd2\": \"int64\",\n  \"groupd3\": \"int64\",\n  \"lhouse_val\": \"float64\",\n  \"snow_groupd2\": \"float64\",\n  \"snow_groupd3\": \"float64\"\n }\n}\n\n# Question\nWhat is the causal effect of having a sow insured on the subsequent number of sows owned by a farmer?","response":"Linha de base ingênua: regressão do resultado no tratamento, sem controles.\ninfer__ols({\"dataset\": \"cai_effect_microinsurance\", \"y\": \"sow3\", \"x\": [\"insuredpig\"]})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_594942d3","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":"cai_effect_microinsurance","y":"sow3","x":["insuredpig"]}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"68d282f9beefbdbcc4939753cfb282a744f9bae5e1b3c575b0563513eb1bbd1f","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["74b7433632c33f14caaf167c27d262fecddb48674895294c466118617af16b1f"],"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":0.8725831285119057}}
{"call_id":"call_68c19b7f","run_id":"20261015T171142Z-suite-causcibench_real-naive-d6494d/causcibench_real.q125/e1a1","step":"analysis","kind":"tool","name":"infer.ols","version":"0.1.0","code_version":"2925aeab2262","inputs":{"dataset":"cai_effect_microinsurance","y":"sow3","x":["insuredpig"],"controls":[],"fe":[],"cluster":null,"se":"HC1","weights":null,"subset":null,"intercept":true,"level":0.95},"input_digest":"96585fbb20ccad10946a395026918970ce95de0389f5c2442eaddfde3ae16ee4","dataset_digests":{"cai_effect_microinsurance":"cdd6961e1779d8abe1e6f2ed9404f07595871ef43fe70583c3308e396368b57a"},"result":{"tool":"infer.ols","version":"0.1.0","call_id":"call_68c19b7f","status":"ok","reason":"","figures":[{"key":"coef.insuredpig","value":1.2152831647206284,"unit":"","label":"Coefficient on insuredpig in the regression of sow3","se":0.0782380883735985,"ci_low":1.0613553287174342,"ci_high":1.3692110007238225,"ci_level":0.95,"derivation":{"steps":[{"text_pt":"Coeficiente de insuredpig na regressão por mínimos quadrados ordinários de sow3.","formula":"","terms":[{"symbol":"n","value":321.0,"unit":"observações","note":""},{"symbol":"k","value":2.0,"unit":"parâmetros","note":""}],"result":1.2152831647206284,"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":0.006121198472355008,"unit":"","note":"diagonal da matriz de covariância"}],"result":0.0782380883735985,"unit":""},{"text_pt":"Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 319 graus de liberdade vezes o erro-padrão.","formula":"IC = β ± c · ep","terms":[{"symbol":"β","value":1.2152831647206284,"unit":"","note":""},{"symbol":"c","value":1.9674283869023719,"unit":"","note":"quantil 0.975"},{"symbol":"ep","value":0.0782380883735985,"unit":"","note":""}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"n_obs","value":321,"flag":false,"note":"observations used"},{"name":"k","value":2,"flag":false,"note":"parameters estimated, fixed-effect dummies included"},{"name":"r2","value":0.6679869119932953,"flag":false,"note":""},{"name":"df_resid","value":319,"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":11.139395423198616,"se":1.7103347707272123,"t":6.512991265717114,"p":2.865103404778542e-10,"ci_low":7.774434244163739,"ci_high":14.504356602233493},{"term":"insuredpig","coef":1.2152831647206284,"se":0.0782380883735985,"t":15.53313980420215,"p":6.53231895830894e-41,"ci_low":1.0613553287174342,"ci_high":1.3692110007238225}]},"datasets":{},"warnings":["159 rows were dropped because one of 'sow3', 'insuredpig' 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":"1ab75d3bef2a073d2a8e38407ce9866dabd75ca0f7df7d5ae6246161861d557f","params_versions":[],"sources":[],"started_at":"2026-10-15T17:11:45.093214Z","duration_ms":1.937916036695242,"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_594942d3","llm_name":"infer__ols","vendor_id":"script_1","result_for_model_ref":"9ea3339b050109b5376bc6a1694c42d33da1e8ece520a580b4b70d5e8056b467"}}
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{"call_id":"call_c7af0156","run_id":"20261015T171142Z-suite-causcibench_real-naive-d6494d/causcibench_real.q125/e1a1","step":"analysis","kind":"note","name":"analyst.guard","version":"","code_version":"2925aeab2262","inputs":{},"input_digest":"","dataset_digests":{},"result":null,"outputs":null,"output_digest":"","params_versions":[],"sources":[],"started_at":"2026-10-15T17:11:45.096945Z","duration_ms":0.0,"error":null,"decided_by":"code","rationale":"","cached":false,"informed_by":[],"model":null,"prompt":null,"response":null,"tokens_in":null,"tokens_out":null,"meta":{"turn_id":"call_357382a3","numbers_found":["1,215"],"unbacked":[],"uncited":[],"invalid_refs":[],"empty":false,"verdict":"accepted","retries_left":1,"vendor_id":"script_2"}}