{"call_id":"call_e766cfd9","run_id":"20261015T171059Z-suite-causcibench_real-scripted-ded01d/causcibench_real.q028/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":"2026-10-15T17:11:00.296577Z","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\": \"card_minimum_wages\",\n \"rows\": 820,\n \"description\": \"The dataset was collected to examine the effects of a minimum wage increase on fast food employment. It includes two survey waves from 410 fast food restaurants such as Burger King, KFC, Wendy's, and Roy Rogers in New Jersey and Pennsylvania. The first wave took place just before the April 1992 wage increase, and the second occurred seven to eight months later. The survey gathered information on employment, wages, prices, store characteristics, and location. Data collection involved phone interviews. The fast food sector was selected for its high share of low-wage jobs, standardization, and compliance with wage laws.\\n\\nThe variables are:\\n\\nsheet: unique store identifier\\nchain: chain identifier (1 = Burger King, 2 = KFC, 3 = Roy Rogers, 4 = Wendy's)\\nco_owned: 1 if the store is company-owned, 0 otherwise\\nstate: 1 if the store is in New Jersey, 0 if in Pennsylvania\\nafter: 1 if the data was collected after an increase in minimum wage, 0 otherwise\\nfte: number of full time employees\\nsouthj: 1 if the store is in southern New Jersey\\ncentralj: 1 if the store is in central New Jersey\\nnorthj: 1 if the store is in northern New Jersey\\npa1: 1 if the store is in northeast suburbs of Philadelphia\\npa2: 1 if the store is in Easton or nearby areas in Pennsylvania\\nshore: 1 if the store is on the New Jersey shore\\nncalls: number of callbacks before completing the first interview\\nempft: number of full-time employees (first interview)\\nemppt: number of part-time employees (first interview)\\nnmgrs: number of managers or assistant managers (first interview)\\nwage_st: starting wage in dollars per hour (first interview)\\ninctime: months until usual first raise (first interview)\\nfirstinc: amount of usual first raise in dollars per hour (first interview)\\nbonus: 1 if the store offers a cash bonus for new workers\\npctaff: percent of employees affected by the new minimum wage\\nmeals: code for meal benefits (0 = none, 1 = free, 2 = reduced price, 3 = both)\\nopen: hour the store opens (first interview)\\nhrsopen: number of hours the store is open per day (first interview)\\npsoda: price of a medium soda, including tax (first interview)\\npfry: price of small fries, including tax (first interview)\\npentree: price of an entree including tax (first interview)\\nnregs: number of cash registers in the store (first interview)\\nnregs11: number of registers open at 11:00 am (first interview)\\ntype2: mode of second interview (1 = phone, 2 = personal)\\nstatus2: status of second interview (0 = refused, 1 = answered, 2-5 = closed for various reasons)\\ndate2: date of second interview (mmddyy format)\\nncalls2: number of callbacks before completing the second interview\\nempft2: number of full-time employees (second interview)\\nemppt2: number of part-time employees (second interview)\\nnmgrs2: number of managers or assistant managers (second interview)\\nwage_st2: starting wage in dollars per hour (second interview)\\ninctime2: months until usual first raise (second interview)\\nfirstin2: amount of usual first raise in dollars per hour (second interview)\\nspecial2: 1 if the store has a special program for new workers (second interview)\\nmeals2: code for meal benefits (second interview; same coding as meals)\\nopen2r: hour the store opens (second interview)\\nhrsopen2: number of hours the store is open per day (second interview)\\npsoda2: price of a medium soda, including tax (second interview)\\npfry2: price of small fries, including tax (second interview)\\npentree2: price of an entree including tax (second interview)\\nnregs2: number of cash registers in the store (second interview)\\nnregs112: number of registers open at 11:00 am (second interview)\",\n \"columns\": {\n  \"sheet\": \"int64\",\n  \"chain\": \"int64\",\n  \"co_owned\": \"int64\",\n  \"state\": \"int64\",\n  \"southj\": \"int64\",\n  \"centralj\": \"int64\",\n  \"northj\": \"int64\",\n  \"pa1\": \"int64\",\n  \"pa2\": \"int64\",\n  \"shore\": \"int64\",\n  \"ncalls\": \"int64\",\n  \"empft\": \"float64\",\n  \"emppt\": \"float64\",\n  \"nmgrs\": \"float64\",\n  \"wage_st\": \"float64\",\n  \"inctime\": \"float64\",\n  \"firstinc\": \"float64\",\n  \"bonus\": \"int64\",\n  \"pctaff\": \"float64\",\n  \"meals\": \"int64\",\n  \"open\": \"float64\",\n  \"hrsopen\": \"float64\",\n  \"psoda\": \"float64\",\n  \"pfry\": \"float64\",\n  \"pentree\": \"float64\",\n  \"nregs\": \"float64\",\n  \"nregs11\": \"float64\",\n  \"type2\": \"int64\",\n  \"status2\": \"int64\",\n  \"date2\": \"int64\",\n  \"ncalls2\": \"float64\",\n  \"empft2\": \"float64\",\n  \"emppt2\": \"float64\",\n  \"nmgrs2\": \"float64\",\n  \"wage_st2\": \"float64\",\n  \"inctime2\": \"float64\",\n  \"firstin2\": \"float64\",\n  \"special2\": \"float64\",\n  \"meals2\": \"float64\",\n  \"open2r\": \"float64\",\n  \"hrsopen2\": \"float64\",\n  \"psoda2\": \"float64\",\n  \"pfry2\": \"float64\",\n  \"pentree2\": \"float64\",\n  \"nregs2\": \"float64\",\n  \"nregs112\": \"float64\",\n  \"fte\": \"float64\",\n  \"post\": \"int64\"\n }\n}\n\n# Question\nDid an increase in minimum wage decrease employment?","response":"causcibench_metadata_rule/1: did\ninfer__did_2x2({\"dataset\": \"card_minimum_wages\", \"y\": \"fte\", \"treated\": \"state\", \"post\": \"post\", \"controls\": []})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_e766cfd9","text":"causcibench_metadata_rule/1: did","tool_calls":[{"vendor_id":"script_1","llm_name":"infer__did_2x2","tool_id":"infer.did_2x2","args":{"dataset":"card_minimum_wages","y":"fte","treated":"state","post":"post","controls":[]}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"82678a5866d3b4f50c1c65e2a37bc5a059f922a8d2a5bc60e442141adf3bcf3c","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["2e124368dd62547fce47b9ca358251bc8c287725befe410aa0c459806cdeac48"],"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":0.6948329973965883}}
{"call_id":"call_9fa80e13","run_id":"20261015T171059Z-suite-causcibench_real-scripted-ded01d/causcibench_real.q028/e1a1","step":"analysis","kind":"tool","name":"infer.did_2x2","version":"0.1.0","code_version":"2925aeab2262","inputs":{"dataset":"card_minimum_wages","y":"fte","treated":"state","post":"post","controls":[],"cluster":null,"level":0.95},"input_digest":"02e7da3196a2b428e5755cd2b0098b1036a02e0f6434fcb48db9550f503ba585","dataset_digests":{"card_minimum_wages":"9ecd0139eedcfa298eb4f431b84bcea62de46362ee01b1f2e959eaddda54d225"},"result":{"tool":"infer.did_2x2","version":"0.1.0","call_id":"call_9fa80e13","status":"ok","reason":"","figures":[{"key":"did","value":2.7536057829805816,"unit":"","label":"Difference-in-differences effect on fte","se":1.7954508096057529,"ci_low":-0.7708127834244074,"ci_high":6.2780243493855705,"ci_level":0.95,"derivation":{"steps":[{"text_pt":"Variação da média do grupo tratado menos a variação da média do grupo de comparação.","formula":"DiD = (ȳ_T1 − ȳ_T0) − (ȳ_C1 − ȳ_C0)","terms":[{"symbol":"ȳ_T1","value":21.02742946708464,"unit":"","note":"tratado, depois"},{"symbol":"ȳ_T0","value":20.439408099688475,"unit":"","note":"tratado, antes"},{"symbol":"ȳ_C1","value":21.165584415584416,"unit":"","note":"comparação, depois"},{"symbol":"ȳ_C0","value":23.33116883116883,"unit":"","note":"comparação, antes"}],"result":2.7536057829805816,"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":3.2236436097139536,"unit":"","note":"diagonal da matriz de covariância"}],"result":1.7954508096057529,"unit":""},{"text_pt":"Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 790 graus de liberdade vezes o erro-padrão.","formula":"IC = DiD ± c · ep","terms":[{"symbol":"DiD","value":2.7536057829805816,"unit":"","note":""},{"symbol":"c","value":1.9629713872132675,"unit":"","note":"quantil 0.975"},{"symbol":"ep","value":1.7954508096057529,"unit":"","note":""}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"design","value":"stacked","flag":false,"note":"stacked rows: treated × post interaction"},{"name":"n_obs","value":794,"flag":false,"note":""},{"name":"se_type","value":"HC1","flag":false,"note":"heteroskedasticity-robust standard errors (HC1)"},{"name":"parallel_trends","value":"untestable","flag":false,"note":"with two periods parallel trends cannot be tested; the estimate rests on assuming them"},{"name":"n_clusters","value":null,"flag":false,"note":"errors not clustered"}],"tables":{"means":[{"group":"treated","period":"before","mean":20.439408099688475,"n":321},{"group":"treated","period":"after","mean":21.02742946708464,"n":319},{"group":"comparison","period":"before","mean":23.33116883116883,"n":77},{"group":"comparison","period":"after","mean":21.165584415584416,"n":77}]},"datasets":{},"warnings":["26 rows were dropped because one of 'fte', 'state', 'post' was missing","with two periods parallel trends cannot be tested"],"assumptions":["without the policy the treated group's outcome would have changed as much as the comparison group's (parallel trends)","no other change hit one group and not the other between the two periods"],"notes":""},"outputs":null,"output_digest":"2792ba760c40142739a3a7fc1782b07704aa95bb8c3dddab3d41fb6f3c8b0033","params_versions":[],"sources":[],"started_at":"2026-10-15T17:11:00.299465Z","duration_ms":31.243958044797182,"error":null,"decided_by":"model","rationale":"causcibench_metadata_rule/1: did","cached":false,"informed_by":[],"model":null,"prompt":null,"response":null,"tokens_in":null,"tokens_out":null,"meta":{"turn_id":"call_e766cfd9","llm_name":"infer__did_2x2","vendor_id":"script_1","result_for_model_ref":"b4ef6d630faa677b5b81cece4aa1f48feab92f0fbe51638e8857854570aa1243"}}
{"call_id":"call_f8033038","run_id":"20261015T171059Z-suite-causcibench_real-scripted-ded01d/causcibench_real.q028/e1a1","step":"analysis","kind":"llm","name":"llm.analyst.scripted","version":"","code_version":"2925aeab2262","inputs":{},"input_digest":"","dataset_digests":{},"result":null,"outputs":null,"output_digest":"","params_versions":[],"sources":[],"started_at":"2026-10-15T17:11:00.330837Z","duration_ms":0.0,"error":null,"decided_by":"model","rationale":"","cached":false,"informed_by":[],"model":"reference","prompt":null,"response":"submit_answer({\"answer\": \"Estimativa (infer.did_2x2): did = 2,754.\", \"figure_refs\": [\"call_9fa80e13:did\"], \"caveats\": []})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_f8033038","text":"","tool_calls":[{"vendor_id":"script_2","llm_name":"submit_answer","tool_id":null,"args":{"answer":"Estimativa (infer.did_2x2): did = 2,754.","figure_refs":["call_9fa80e13:did"],"caveats":[]}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"82678a5866d3b4f50c1c65e2a37bc5a059f922a8d2a5bc60e442141adf3bcf3c","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["2e124368dd62547fce47b9ca358251bc8c287725befe410aa0c459806cdeac48","191f8a0eb285302562592e487eb98d678d122a6dffc75b861e98577613f825b2","98c3e2ce3d4d85dd2770faea91629bf64812eb18f0900b8d5515d01b9716eb0e"],"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":0.2341249492019415}}
{"call_id":"call_31ef498e","run_id":"20261015T171059Z-suite-causcibench_real-scripted-ded01d/causcibench_real.q028/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":"2026-10-15T17:11:00.332506Z","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_f8033038","numbers_found":["2,754"],"unbacked":[],"uncited":[],"invalid_refs":[],"empty":false,"verdict":"accepted","retries_left":1,"vendor_id":"script_2"}}