{"call_id":"call_8b9b88ee","run_id":"20261015T171059Z-suite-causcibench_real-scripted-ded01d/causcibench_real.q084/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":"2026-10-15T17:11:01.311098Z","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\": \"xu_does_mislabelling\",\n \"rows\": 1200,\n \"description\": \"This dataset comes from an online survey experiment conducted in the United States on Amazon Mechanical Turk (MTurk) in March 2020, during the early stages of the COVID-19 pandemic. A total of 1,200 adult participants were included after excluding incomplete responses and failed manipulation checks. Respondents were randomly assigned to one of two groups: a control group where the virus was labeled COVID-19 and a treatment group where it was labeled the \\\"\\\"Chinese Virus\\\"\\\". The goal of the experiment was to test whether these labels influenced attitudes toward Chinese immigrants and shifted blame toward or away from the federal government. To capture sensitive views, the design combined a list experiment with direct survey questions.\\n\\nDataset Variables:\\nduration: the time subjects spent in survey\\ncblame_1: blame in COVID19 group\\ntblame_1: blame in \\\"\\\"Chinese Virus\\\"\\\" group\\nmc: answer of the manipulation check question\\nlc_c: list item count in 3-item COVID19 group\\ndirect_c: answer of the direct question in the 3-item COVID19 group\\nlt_c: list item count in 4-item COVID19 group\\nlc_t: list item count in 3-item \\\"\\\"Chinese Virus\\\"\\\" group\\ndirect_t: answer of the direct question in the 3-item \\\"\\\"Chinese Virus\\\"\\\" group\\nlt_t: list item count in 4-item \\\"\\\"Chinese Virus\\\"\\\" group\\nsex: 1=male; 0=female\\nage: subjects' age\\nrace: subjects' race\\nincome: subjects' income\\nworry: how worried subjects are about COVID-19 exposure\\ntwitter: frequency of using Twitter/Facebook\\nparty: party affiliation\\nlean: party lean\\nideology: political ideology\\nstate: subjects' location state\\nch: 1=\\\"\\\"Chinese virus\\\"\\\" group, 0=COVID19 group\\ncovid: item number in COVID19 group (1=4-item; 0=3-item)\\nvirus: item number in \\\"\\\"Chinese Virus\\\"\\\" group (1=4-item; 0=3-item)\\nlist: item subgroup (1=4-item group; 0=3-item group)\\nblame: blame\\ndirect: answer of direct question\\nict.c: item count in COVID19 group\\nict.t: item count in \\\"\\\"Chinese Virus\\\"\\\" group\\nict: item count\\nY.c: social desirability measure in COVID19 group\\nY.t: social desirability measure in \\\"\\\"Chinese Virus\\\"\\\" group\\nido: numeric ideology measure\\nido1: simplified measure of ideology\\ninc: numeric income measure\\nwor: numeric measure of worry\\nsocial: numeric measure of social media use\\nID: subject ID\\nitem: 3-item or 4-item\\nch1: a=COVID19; b=\\\"\\\"Chinese Virus\\\"\\\"\\nmale: 1=male; 0=female\\nfemale: 1=female; 0=male\\nyoung: 1=age<=29\\nmidage: 1=29<age<=49\\nold: 1=age>49\\nwhite: 1=white; 0=not white\\nblk: 1=black; 0=not black\\nhis: 1=hispanic; 0=not hispanic\\nasian: 1=asian; 0=not asian\\nother: 1=other; 0=not other\\nhwor: 1=worried or very worried; 0=not too worried or not worried at all\\nlwor: 0=worried or very worried; 1=not too worried or not worried at all\\nhsocial: 1=\\\"\\\"social\\\"\\\">=2; 0=\\\"\\\"social\\\"\\\"<2\\nlsocial: 1=\\\"\\\"social\\\"\\\"<2; 0=\\\"\\\"social\\\"\\\">=2\\nDem: 1=Democrat; 0=other\\nRep: 1=Republican; 0=other\\nInd: 1=Independent; 0=other\\nlib: 1=Liberal; 0=other\\ncon: 1=Conservative; 0=other\\nmod: 1=Moderate; 0=other\\nlow: 1=Less than $25,000; 0=other\\nmed: 1=\\\"\\\"inc\\\"\\\">=2 and <5; 0=other\\nhigh: 1=\\\"\\\"inc\\\"\\\">=5; 0=other\\nmcr: 1=answer correct in manipulation check question; 0=answer wrong\\ngroup: covid3=COVID19 3-item group; covid4=COVID19 4-item group; virus3=\\\"\\\"Chinese Virus\\\"\\\" 3-item group; virus4=\\\"\\\"Chinese Virus\\\"\\\" 4-item group\",\n \"columns\": {\n  \"X\": \"int64\",\n  \"duration\": \"int64\",\n  \"cblame_1\": \"float64\",\n  \"tblame_1\": \"float64\",\n  \"mc\": \"str\",\n  \"lc_c\": \"float64\",\n  \"direct_c\": \"float64\",\n  \"lt_c\": \"float64\",\n  \"lc_t\": \"float64\",\n  \"direct_t\": \"float64\",\n  \"lt_t\": \"float64\",\n  \"sex\": \"int64\",\n  \"age\": \"int64\",\n  \"race\": \"str\",\n  \"income\": \"str\",\n  \"worry\": \"str\",\n  \"twitter\": \"str\",\n  \"party\": \"str\",\n  \"lean\": \"str\",\n  \"ideology\": \"str\",\n  \"state\": \"str\",\n  \"ch\": \"int64\",\n  \"covid\": \"float64\",\n  \"virus\": \"float64\",\n  \"list\": \"int64\",\n  \"blame\": \"float64\",\n  \"direct\": \"float64\",\n  \"ict.c\": \"float64\",\n  \"ict.t\": \"float64\",\n  \"ict\": \"int64\",\n  \"Y.c\": \"float64\",\n  \"Y.t\": \"float64\",\n  \"ido\": \"int64\",\n  \"ido1\": \"int64\",\n  \"inc\": \"int64\",\n  \"wor\": \"int64\",\n  \"social\": \"int64\",\n  \"ID\": \"int64\",\n  \"Item\": \"str\",\n  \"ch1\": \"str\",\n  \"male\": \"int64\",\n  \"female\": \"int64\",\n  \"young\": \"int64\",\n  \"midage\": \"int64\",\n  \"old\": \"int64\",\n  \"white\": \"int64\",\n  \"blk\": \"int64\",\n  \"his\": \"int64\",\n  \"asian\": \"int64\",\n  \"other\": \"int64\",\n  \"hwor\": \"int64\",\n  \"lwor\": \"int64\",\n  \"hsocial\": \"int64\",\n  \"lsocial\": \"int64\",\n  \"Dem\": \"int64\",\n  \"Rep\": \"int64\",\n  \"Ind\": \"int64\",\n  \"lib\": \"int64\",\n  \"con\": \"int64\",\n  \"mod\": \"int64\",\n  \"low\": \"int64\",\n  \"med\": \"int64\",\n  \"high\": \"int64\",\n  \"mcr\": \"int64\",\n  \"group\": \"str\"\n }\n}\n\n# Question\nDoes labeling COVID-19 as the 'Chinese Virus' causally affect the level of blame the public assigns to the federal government?","response":"causcibench_metadata_rule/1: ols\ninfer__ols({\"dataset\": \"xu_does_mislabelling\", \"y\": \"blame\", \"x\": [\"ch\"], \"controls\": []})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_8b9b88ee","text":"causcibench_metadata_rule/1: ols","tool_calls":[{"vendor_id":"script_1","llm_name":"infer__ols","tool_id":"infer.ols","args":{"dataset":"xu_does_mislabelling","y":"blame","x":["ch"],"controls":[]}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"c1f32b32f40f6b3f93158632162f9284b63c06da6fd563e1bfbd1c0ad260a18e","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["3fd37b7dca6f37f4b42a3a8a4170db47d464da2845d476f0b3be2606a52303d0"],"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":0.7110000587999821}}
{"call_id":"call_68e1c275","run_id":"20261015T171059Z-suite-causcibench_real-scripted-ded01d/causcibench_real.q084/e1a1","step":"analysis","kind":"tool","name":"infer.ols","version":"0.1.0","code_version":"2925aeab2262","inputs":{"dataset":"xu_does_mislabelling","y":"blame","x":["ch"],"controls":[],"fe":[],"cluster":null,"se":"HC1","weights":null,"subset":null,"intercept":true,"level":0.95},"input_digest":"9f0283c4a6f2e6e1aba324dfcf882bca06104fd3628e3bfeb9ea50f6d3de35b3","dataset_digests":{"xu_does_mislabelling":"4ed38f68ca3d0323406ceb338cfe173ce776e8166aa30250c169c61662b6c20f"},"result":{"tool":"infer.ols","version":"0.1.0","call_id":"call_68e1c275","status":"ok","reason":"","figures":[{"key":"coef.ch","value":0.9868697099037835,"unit":"","label":"Coefficient on ch in the regression of blame","se":1.8086183986590711,"ci_low":-2.561545184257393,"ci_high":4.535284604064961,"ci_level":0.95,"derivation":{"steps":[{"text_pt":"Coeficiente de ch na regressão por mínimos quadrados ordinários de blame.","formula":"","terms":[{"symbol":"n","value":1199.0,"unit":"observações","note":""},{"symbol":"k","value":2.0,"unit":"parâmetros","note":""}],"result":0.9868697099037835,"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":3.2711005119681027,"unit":"","note":"diagonal da matriz de covariância"}],"result":1.8086183986590711,"unit":""},{"text_pt":"Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 1197 graus de liberdade vezes o erro-padrão.","formula":"IC = β ± c · ep","terms":[{"symbol":"β","value":0.9868697099037835,"unit":"","note":""},{"symbol":"c","value":1.9619478032469477,"unit":"","note":"quantil 0.975"},{"symbol":"ep","value":1.8086183986590711,"unit":"","note":""}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"n_obs","value":1199,"flag":false,"note":"observations used"},{"name":"k","value":2,"flag":false,"note":"parameters estimated, fixed-effect dummies included"},{"name":"r2","value":0.0002487427166812939,"flag":false,"note":""},{"name":"df_resid","value":1197,"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":49.38269550748752,"se":1.2393688679866675,"t":39.845034664868436,"p":1.0735948976427613e-221,"ci_low":46.95111847952842,"ci_high":51.81427253544662},{"term":"ch","coef":0.9868697099037835,"se":1.8086183986590711,"t":0.5456483858814326,"p":0.58540932290685,"ci_low":-2.561545184257393,"ci_high":4.535284604064961}]},"datasets":{},"warnings":["1 rows were dropped because one of 'blame', 'ch' 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":"2f722b3dffe6fc056c0b1512ee87687a12e07e2b91da146b38a02d2df6f9e3c9","params_versions":[],"sources":[],"started_at":"2026-10-15T17:11:01.313901Z","duration_ms":3.509625094011426,"error":null,"decided_by":"model","rationale":"causcibench_metadata_rule/1: ols","cached":false,"informed_by":[],"model":null,"prompt":null,"response":null,"tokens_in":null,"tokens_out":null,"meta":{"turn_id":"call_8b9b88ee","llm_name":"infer__ols","vendor_id":"script_1","result_for_model_ref":"dfbde396a71243351236f2d53820dafe90c965895cb6be321491ef9abca8ed4a"}}
{"call_id":"call_ad8a8e4a","run_id":"20261015T171059Z-suite-causcibench_real-scripted-ded01d/causcibench_real.q084/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":"2026-10-15T17:11:01.317609Z","duration_ms":0.0,"error":null,"decided_by":"model","rationale":"","cached":false,"informed_by":[],"model":"reference","prompt":null,"response":"submit_answer({\"answer\": \"Estimativa (infer.ols): coef.ch = 0,9869.\", \"figure_refs\": [\"call_68e1c275:coef.ch\"], \"caveats\": []})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_ad8a8e4a","text":"","tool_calls":[{"vendor_id":"script_2","llm_name":"submit_answer","tool_id":null,"args":{"answer":"Estimativa (infer.ols): coef.ch = 0,9869.","figure_refs":["call_68e1c275:coef.ch"],"caveats":[]}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"c1f32b32f40f6b3f93158632162f9284b63c06da6fd563e1bfbd1c0ad260a18e","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["3fd37b7dca6f37f4b42a3a8a4170db47d464da2845d476f0b3be2606a52303d0","df7f8a08493adc10cca9aa3513c98146ddab2e6f88be1c7a4ff8c2fc09829210","5601e98d9382cd409b12d9c742b8d7ef544c869534b0bd9e74e3b35a90d5e78f"],"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":0.3451250959187746}}
{"call_id":"call_5439683c","run_id":"20261015T171059Z-suite-causcibench_real-scripted-ded01d/causcibench_real.q084/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":"2026-10-15T17:11:01.319524Z","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_ad8a8e4a","numbers_found":["0,9869"],"unbacked":[],"uncited":[],"invalid_refs":[],"empty":false,"verdict":"accepted","retries_left":1,"vendor_id":"script_2"}}