{"call_id":"call_0ea9565f","run_id":"20261015T171122Z-suite-causcibench_real-baseline-4f971b/causcibench_real.q152/e1a1","step":"analysis","kind":"llm","name":"llm.analyst.baseline","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":"2026-10-15T17:11:26.347446Z","duration_ms":0.0,"error":null,"decided_by":"model","rationale":"","cached":false,"informed_by":[],"model":"rules-1","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\": \"chi_improving\",\n \"rows\": 44362,\n \"description\": \"This dataset comprises comprehensive academic records from a large Canadian university system, consisting of one central campus and two smaller satellite campuses. The data tracks 12,530 first-year students who entered the university between the 1996-97 and 2003-04 academic years, with students aged 17-21 at entry. The dataset focuses on students whose academic performance places them near institutional academic standing thresholds, specifically those with first-year GPAs within a range of the university's academic evaluation criteria. Different campuses maintain distinct GPA cutoffs for academic standing assessments, resulting in variations in institutional policies across the university system. Students are placed on academic probation if their GPA falls below the cutoffs. The longitudinal structure captures academic performance across multiple terms and years, including semester and cumulative GPAs, total credits attempted and completed, registration patterns for fall and summer terms, and academic standing determinations. Outcome measures track graduation status at 4, 5, and 6-year intervals, along with probation and suspension incidents.\\n\\nVariable description:\\nwinter_ac_standing: winter academic standing status (OKAY or PROB (indicating probation))\\nGPA_year1: grade point average by academic year 1\\nGPA_year2: grade point average for academic year 2\\nCGPA_year1: cumulative grade point average for year 1\\nCGPA_year2: cumulative grade point average for year 2\\ntotcredits_year1: total academic credits attempted in year 1\\ntotcredits_year2: total academic credits attempted in year 2\\nwinter_ac_stand_year1: winter academic standing status in year 1 (OKAY or PROB (indicating probation))\\nwinter_ac_stand_year2: winter academic standing status in year 2 (OKAY or PROB (indicating probation))\\ngoodstanding_year1: binary representation of winter_ac_stand_year1; Indicator for academic standing (1 = OKAY, 0 = PROB)\\ngoodstanding_year2: binary representation of winter_ac_stand_year2; Indicator for academic standing (1 = OKAY, 0 = PROB)\\nfallreg_year1: fall term registration status in year 1 (1 = registered, 0 = not registered)\\nfallreg_year2: fall term registration status in year 2 (1 = registered, 0 = not registered)\\nsummerreg_year1: summer term registration status in year 1\\nsummerreg_year2: summer term registration status in year 2\\nsex: Sex of the student (MALE / FEMALE)\\nfemale: 1 = FEMALE, 0 = otherwise\\nmale: 1 = MALE, 0 = otherwise\\nmtongue: mother tongue language\\nage_at_entry: student age at university entry\\nbpl_north_america, bpl_asia, bpl_other: Binary variables to indicate birth place regions (1 = born in that region, 0 = not born in that region)\\ncanadian: Canadian citizenship status (1 = yes, 0 = no)\\nfirstyear: year the student first entered the university\\nyearstodegree: time taken to complete the degree\\ngradin4, gradin5, gradin6: Binary variables that indicate if the student graduated within the specified years\\ncumcredits1, cumcredits2: cumulative credits completed in years 1 and 2\\nprog_science/prog_socsci_bus/prog_socscihum: binary variable that indicates the academic program of the student: science, social science, humanities\\nenglish, french, other_lang: binary variable indicating whether the student's main language is English, French, or some other language\\ngpacutoff: GPA evaluation threshold\\ndist_from_cut: distance from academic threshold\\nprobation_year1: probation status by year 1\\nprobation_year2: probation status by year 2\\nsuspended_year1: suspension status by year 1\\nsuspended_year2: suspension status by year 2\\nloc_campus1, loc_campus2, loc_campus3: Binary indicators for campus location\\nhsgrade_pct: high school grade percentile\\nveryhighHS, highHS, lowHS: Binary indicators for high school performance\\nhsgrade_pct: overall percentage achieved in high school\\nleft_school: binary variable representing whether the student left school (1) or not (0)\",\n \"columns\": {\n  \"credits1\": \"float64\",\n  \"credits2\": \"float64\",\n  \"credits_earned1\": \"float64\",\n  \"credits_earned2\": \"float64\",\n  \"session1\": \"int64\",\n  \"winter_ac_standing\": \"str\",\n  \"GPA_year1\": \"float64\",\n  \"GPA_year2\": \"float64\",\n  \"CGPA_year1\": \"float64\",\n  \"CGPA_year2\": \"float64\",\n  \"totcredits_year1\": \"float64\",\n  \"totcredits_year2\": \"float64\",\n  \"goodstanding_year1\": \"int64\",\n  \"goodstanding_year2\": \"int64\",\n  \"winter_ac_stand_year1\": \"str\",\n  \"winter_ac_stand_year2\": \"str\",\n  \"fallreg_year1\": \"int64\",\n  \"fallreg_year2\": \"int64\",\n  \"act_fallreg_year1\": \"str\",\n  \"act_fallreg_year2\": \"str\",\n  \"summerreg_year1\": \"int64\",\n  \"summerreg_year2\": \"int64\",\n  \"sex\": \"str\",\n  \"mtongue\": \"str\",\n  \"identifier\": \"int64\",\n  \"age_at_entry\": \"float64\",\n  \"bpl_north_america\": \"float64\",\n  \"bpl_asia\": \"float64\",\n  \"canadian\": \"float64\",\n  \"bpl_other\": \"float64\",\n  \"firstyear\": \"int64\",\n  \"yearstodegree\": \"float64\",\n  \"gradin4\": \"float64\",\n  \"gradin5\": \"float64\",\n  \"gradin6\": \"float64\",\n  \"gradin4orcont\": \"float64\",\n  \"cumcredits1\": \"float64\",\n  \"cumcredits2\": \"float64\",\n  \"prog_science\": \"float64\",\n  \"prog_socsci_bus\": \"float64\",\n  \"prog_socscihum\": \"float64\",\n  \"prog_unknown\": \"float64\",\n  \"english\": \"float64\",\n  \"noenglish\": \"float64\",\n  \"french\": \"float64\",\n  \"other_lang\": \"float64\",\n  \"female\": \"float64\",\n  \"male\": \"float64\",\n  \"gpacutoff\": \"float64\",\n  \"dist_from_cut\": \"float64\",\n  \"year2_dist_from_cut\": \"float64\",\n  \"CGPA_final\": \"float64\",\n  \"nextGPA\": \"float64\",\n  \"nextCGPA\": \"float64\",\n  \"probation_year1\": \"float64\",\n  \"probation_year2\": \"float64\",\n  \"probation_summer1\": \"float64\",\n  \"probation_summer2\": \"float64\",\n  \"probation_ever\": \"float64\",\n  \"suspended_year1\": \"float64\",\n  \"suspended_summer1\": \"float64\",\n  \"suspended_year2\": \"float64\",\n  \"suspended_summer2\": \"float64\",\n  \"suspended_ever\": \"float64\",\n  \"loc_campus1\": \"float64\",\n  \"loc_campus2\": \"float64\",\n  \"loc_campus3\": \"float64\",\n  \"all\": \"float64\",\n  \"left_school\": \"float64\",\n  \"gpalscutoff\": \"float64\",\n  \"gpaXgpalscutoff\": \"float64\",\n  \"gpaXgpagrcutoff\": \"float64\",\n  \"gpasqXgpagrcutoff\": \"float64\",\n  \"dist_from_cut_med10\": \"float64\",\n  \"dist_from_cut_med05\": \"float64\",\n  \"dist_from_cut_med025\": \"float64\",\n  \"hsgrade_pct\": \"int64\",\n  \"veryhighHS\": \"float64\",\n  \"highHS\": \"float64\",\n  \"lowHS\": \"float64\",\n  \"maleXhighHS\": \"float64\",\n  \"femaleXhighHS\": \"float64\",\n  \"maleXlowabil\": \"float64\",\n  \"femaleXlowabil\": \"float64\",\n  \"clustervar\": \"float64\",\n  \"summer\": \"float64\"\n }\n}\n\n# Question\nWhat is the effect of being placed on academic probation after the first year on the probability that a student leaves school?","response":"submit_answer({\"answer\": \"Pelas regras deste roteador não identifiquei um desenho que responda à pergunta; nenhuma estimativa foi feita.\", \"figure_refs\": [], \"caveats\": []})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_0ea9565f","text":"","tool_calls":[{"vendor_id":"baseline_answer","llm_name":"submit_answer","tool_id":null,"args":{"answer":"Pelas regras deste roteador não identifiquei um desenho que responda à pergunta; nenhuma estimativa foi 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