{"call_id":"call_5cc4c0b1","run_id":"20261015T171059Z-suite-causcibench_real-scripted-ded01d/causcibench_real.q042/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":"2026-10-15T17:11:00.615715Z","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\": \"angrist_using_maimonides\",\n \"rows\": 2029,\n \"description\": \"The dataset contains data on 5th graders enrolled in Israeli public elementary schools. The schools had imposed an upper limit on the class size of 40. In case the class size exceeds 40 students, another class is set up. To ensure balance across multiple classes of the same grade, students are split roughly evenly. In addition to including information about the school and the class size, the dataset also includes data on student performance in verbal (Hebrew reading) and mathematics tests.\\nVariables include:\\nschlcode: School identifier code\\ntowncode: Town identifier code\\ntownname: Town name\\ntownid: Town identifier (same as towncode)\\npopcode: Population/administrative code used for linkage\\ngrade: Grade level (e.g., 3, 4, 5)\\nclassid: Class identifier within school and grade\\nc_size: Total number of 5th graders in the school\\nclassize: Total number of students in each class, post-splitting if enrollment is greater than 40\\nc_boys: Number of boys in 5th grade\\nc_girls: Number of girls in 5th grade\\nc_numcl: Total number of classes in the school\\nc_type: Total number of classes in the 5th grade\\ncohsize: Total number of students in the 5th grade (same as c_size)\\nclassct: Count of classes in the 5th grade (same as c_num5rd)\\nc_num5rd: Total number of classes in the 5th grade (same as c_type)\\nmrkgrm5: Average reading (verbal) score for grade 5\\nngrm5: Number of fifth-grade reading test takers\\nnmth5: Number of fifth-grade math test takers\\nmathsize: Number of students who took the math test in the class\\navgmath: Average composite math score in the class (1-100 scale)\\npassmath: Share of students passing the math test in the class\\nverbsize: Number of students who took the reading test in the class\\navgverb: Average composite reading (verbal) score in the class (1-100 scale)\\npassverb: Share of students passing the reading (verbal) test in the class\\nstudchk: Data quality/student consistency check indicator (1 = no missing data)\\nimpute: Indicator that class-level averages were imputed from school totals\\ntip_a: Percent of disadvantaged students (same as tip_s)\\ntip_s: Percent of disadvantaged students\\ntipuach: Percent of disadvantaged students (same as tip_s)\",\n \"columns\": {\n  \"schlcode\": \"int64\",\n  \"c_size\": \"int64\",\n  \"c_boys\": \"int64\",\n  \"c_girls\": \"int64\",\n  \"c_numcl\": \"int64\",\n  \"c_pik\": \"int64\",\n  \"c_status\": \"int64\",\n  \"c_leom\": \"int64\",\n  \"c_tip\": \"float64\",\n  \"c_num5rd\": \"int64\",\n  \"c_type\": \"int64\",\n  \"flgrm5\": \"float64\",\n  \"mrkgrm5\": \"float64\",\n  \"ngrm5\": \"float64\",\n  \"flmth5\": \"float64\",\n  \"mrkmth5\": \"float64\",\n  \"nmth5\": \"float64\",\n  \"towncode\": \"float64\",\n  \"townname\": \"str\",\n  \"popcode\": \"float64\",\n  \"tip_a\": \"float64\",\n  \"grade\": \"int64\",\n  \"classid\": \"int64\",\n  \"classize\": \"int64\",\n  \"_type_\": \"int64\",\n  \"_freq_\": \"int64\",\n  \"cohsize\": \"int64\",\n  \"mathsize\": \"float64\",\n  \"avgmath\": \"float64\",\n  \"passmath\": \"float64\",\n  \"verbsize\": \"float64\",\n  \"avgverb\": \"float64\",\n  \"passverb\": \"float64\",\n  \"studchk\": \"float64\",\n  \"misskov2\": \"int64\",\n  \"missagg\": \"int64\",\n  \"nmiss_k\": \"int64\",\n  \"nmiss_a\": \"int64\",\n  \"classct\": \"int64\",\n  \"math_n\": \"float64\",\n  \"flmath_n\": \"float64\",\n  \"nmath_n\": \"float64\",\n  \"verb_n\": \"float64\",\n  \"flverb_n\": \"float64\",\n  \"nverb_n\": \"float64\",\n  \"impute\": \"int64\",\n  \"nverb_m\": \"float64\",\n  \"nmath_m\": \"float64\",\n  \"tip_s\": \"float64\",\n  \"townid\": \"float64\",\n  \"tipuach\": \"int64\",\n  \"class_section\": \"float64\"\n }\n}\n\n# Question\nDoes class size affect reading scores of students?","response":"causcibench_metadata_rule/1: iv\ninfer__iv_2sls({\"dataset\": \"angrist_using_maimonides\", \"y\": \"avgverb\", \"endog\": [\"classize\"], \"instruments\": [\"class_section\"], \"exog\": [\"tipuach\"]})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_5cc4c0b1","text":"causcibench_metadata_rule/1: iv","tool_calls":[{"vendor_id":"script_1","llm_name":"infer__iv_2sls","tool_id":"infer.iv_2sls","args":{"dataset":"angrist_using_maimonides","y":"avgverb","endog":["classize"],"instruments":["class_section"],"exog":["tipuach"]}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"565912868e8da671f9c7cd4d17a9ae313ed9609f58aa890b392bc36ec9e43f4d","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["b05ba0a28f9408565796b9cd53a4d9b486faf6a65fce4291e351993633a47722"],"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":0.7506250403821468}}
{"call_id":"call_493f21ec","run_id":"20261015T171059Z-suite-causcibench_real-scripted-ded01d/causcibench_real.q042/e1a1","step":"analysis","kind":"tool","name":"infer.iv_2sls","version":"0.1.0","code_version":"2925aeab2262","inputs":{"dataset":"angrist_using_maimonides","y":"avgverb","endog":["classize"],"instruments":["class_section"],"exog":["tipuach"],"cluster":null,"se":"HC1","subset":null,"level":0.95},"input_digest":"b2cb129a6f3386547a217a8494967476aac11e82bfb0526090d8d4847635bceb","dataset_digests":{"angrist_using_maimonides":"872ee3e71c86dc0b6554fa04b52d9469475909091a9fd16f4e2db508d3391e3f"},"result":{"tool":"infer.iv_2sls","version":"0.1.0","call_id":"call_493f21ec","status":"ok","reason":"","figures":[{"key":"coef.classize","value":-0.1562625900388293,"unit":"","label":"2SLS effect of classize on avgverb","se":0.034439262942871265,"ci_low":-0.22380275404245265,"ci_high":-0.08872242603520596,"ci_level":0.95,"derivation":{"steps":[{"text_pt":"Com um instrumento e um regressor endógeno, o MQ2E é a razão entre o coeficiente de class_section na forma reduzida (avgverb sobre class_section e os controles) e no primeiro estágio (classize sobre class_section e os controles).","formula":"β = ρ / π","terms":[{"symbol":"ρ","value":-0.1105779956010705,"unit":"","note":"forma reduzida"},{"symbol":"π","value":0.7076421526968997,"unit":"","note":"primeiro estágio"}],"result":-0.1562625900388293,"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":0.001186062832048226,"unit":"","note":"diagonal da matriz de covariância"}],"result":0.034439262942871265,"unit":""},{"text_pt":"Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 2021 graus de liberdade vezes o erro-padrão.","formula":"IC = β ± c · ep","terms":[{"symbol":"β","value":-0.1562625900388293,"unit":"","note":""},{"symbol":"c","value":1.9611384864902803,"unit":"","note":"quantil 0.975"},{"symbol":"ep","value":0.034439262942871265,"unit":"","note":""}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"first_stage_F.classize","value":1355.1751702192244,"flag":false,"note":"Wald F of the excluded instruments in the first stage for classize (heteroskedasticity-robust standard errors (HC1))"},{"name":"partial_r2.classize","value":0.4502056229987632,"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":2024,"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":84.31208159121093,"se":1.1304479397003213,"t":74.58289641676187,"p":0.0,"ci_low":82.09511662969098,"ci_high":86.52904655273088},{"term":"tipuach","coef":-0.36790966450757867,"se":0.014505616632774084,"t":-25.363255752693835,"p":1.909394658674038e-123,"ci_low":-0.3963571875563855,"ci_high":-0.33946214145877185},{"term":"classize","coef":-0.1562625900388293,"se":0.034439262942871265,"t":-4.537338394786256,"p":6.031354784741715e-6,"ci_low":-0.22380275404245265,"ci_high":-0.08872242603520596}],"first_stage":[{"endog":"classize","instrument":"class_section","coef":0.7076421526968997,"se":0.019222770328194007,"t":36.812703924314285,"p":1.7714317295044745e-227,"ci_low":0.669943637989315,"ci_high":0.7453406674044843}],"reduced_form":[{"instrument":"class_section","coef":-0.1105779956010705,"se":0.023829263134844427,"t":-4.640428660145073,"p":3.699871148752604e-6,"ci_low":-0.1573104806395179,"ci_high":-0.06384551056262307}]},"datasets":{},"warnings":["5 rows were dropped because one of 'avgverb', 'classize', 'class_section', 'tipuach' was missing"],"assumptions":["the instruments affect the outcome only through the endogenous regressors (exclusion restriction)","the instruments are as good as randomly assigned given the controls","errors are independent across observations; their variance may differ"],"notes":""},"outputs":null,"output_digest":"c64d959f2725e05b2464a2de0ba2ee7abcfc9b1099fc0dcebd64e83c36314dc7","params_versions":[],"sources":[],"started_at":"2026-10-15T17:11:00.618550Z","duration_ms":19.52166692353785,"error":null,"decided_by":"model","rationale":"causcibench_metadata_rule/1: iv","cached":false,"informed_by":[],"model":null,"prompt":null,"response":null,"tokens_in":null,"tokens_out":null,"meta":{"turn_id":"call_5cc4c0b1","llm_name":"infer__iv_2sls","vendor_id":"script_1","result_for_model_ref":"c7fd32e918ce24af61e8cb7bdfba838d857fae32d5e053406932bb1b703ac771"}}
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