{"call_id":"call_3b419b4f","run_id":"20261015T171122Z-suite-causcibench_real-baseline-4f971b/causcibench_real.q097/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":"2026-10-15T17:11:24.184640Z","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\": \"wilson_internet\",\n \"rows\": 2063,\n \"description\": \"The data comes from a randomized control trial conducted to assess the impact of Internet-accessed sexually transmitted infection testing (e-STI testing) through the sh24 platform. Sexually active individuals between the ages of 16 and 30 were recruited. The participants were then randomly sent text messages inviting them to participate in the testing. The treatment group was asked to order the test online while the control group was asked to visit a walk-in clinic and take the test. The main outcome of interest was the self-reported diagnosis of STIs, supported by medical reports.\\n\\nVariables include:\\nanon_id: Participant id\\ngroup: Allocation group ('Control' denotes control group and 'SH_24' denotes treatment group)\\nimd_decile: Index of Multiple Deprivation decile rankings on scale of 1-10; 1 represents the most deprived and 10 represents the least deprived\\npartners: Number of sexual partners in past 12 months at baseline\\ngender: Gender (Male or Female)\\nmsm: Men who have sex with men ('msm'=Yes; 'other'=No; 99=Not Known)\\nethnicgrp: Ethnic group\\nage: Age in completed years at baseline\\nanytest_sr: Binary variable indicating whether the participant self-reported the completion of the STI test or not (1=tested, 0=not tested)\\nanydiag_sr: Binary variable indicating whether the participant self-reported the diagnosis of STI test or not (1=diagnosed, 0=not diagnosed)\\nanytreat_sr: Binary variable indicating whether the participant self-reported receiving STI treatment or not (1=treated, 0=not treated)\\nanytest: Objectively verified binary indicator of STI testing completion (1 = tested, 0 = not tested)\\nanydiag: Objectively verified binary indicator of STI diagnosis (1 = diagnosed, 0 = not diagnosed)\\nanytreat: Objectively verified binary indicator of STI treatment (1 = treated, 0 = not treated)\\ntime_test: Time from randomization to test in days\\ntime_treat: Time from randomization to treatment in days\\nsh24_launch: Binary variable indicating whether the randomization occurred before or after SH:24 was made publicly available (1 = yes, 0 = no)\",\n \"columns\": {\n  \"anon_id\": \"int64\",\n  \"group\": \"str\",\n  \"imd_decile\": \"int64\",\n  \"partners\": \"str\",\n  \"gender\": \"str\",\n  \"msm\": \"str\",\n  \"ethnicgrp\": \"str\",\n  \"age\": \"int64\",\n  \"anytest_sr\": \"float64\",\n  \"anydiag_sr\": \"float64\",\n  \"anytreat_sr\": \"float64\",\n  \"anytest\": \"float64\",\n  \"anydiag\": \"float64\",\n  \"anytreat\": \"float64\",\n  \"time_test\": \"float64\",\n  \"time_treat\": \"float64\",\n  \"sh24_launch\": \"str\"\n }\n}\n\n# Question\nWhat is the effect of the e-STI testing service on the probability of getting tested for an STI by 6 weeks?","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_3b419b4f","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 feita.","figure_refs":[],"caveats":[]}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"3abb899648324fedb702d9650b07d87d9393a4fc1dc3dc405df1793c321673da","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["aa1d314f9bedb8dd2ce07f2825764fb3c4cbb67f2206a6e09b78d0457a3448b9"],"config":{}},"attempts":[],"response_id":null,"finish_reason":"tool_use","model_reported":"rules-1","usage":{"input":0,"output":0,"thoughts":0,"cached":0},"thoughts":null,"vendor":false,"wall_ms":0.3813339862972498}}
{"call_id":"call_25ce1ad4","run_id":"20261015T171122Z-suite-causcibench_real-baseline-4f971b/causcibench_real.q097/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":"2026-10-15T17:11:24.186740Z","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_3b419b4f","numbers_found":[],"unbacked":[],"uncited":[],"invalid_refs":[],"empty":false,"verdict":"accepted","retries_left":1,"vendor_id":"baseline_answer"}}