{"call_id":"call_f05cc862","run_id":"20261015T171059Z-suite-causcibench_real-scripted-ded01d/causcibench_real.q174/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":"2026-10-15T17:11:04.548117Z","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\": \"bowles_countering\",\n \"rows\": 868,\n \"description\": \"The data aims to test whether trusted WhatsApp messages can counter COVID-19 misinformation in Zimbabwe. Partnering with Internews and Kubatana, the researchers crafted short weekly messages: Week 1 emphasized transmission and social distancing, Week 2 debunked fake cures. At the WhatsApp broadcast list level, timing was randomized: treatment groups received messages on Monday, control groups on Saturday. To measure effects, a survey was sent on Thursday. Knowledge was assessed through factual questions, and behavior through a list experiment, where respondents received either a short list (only non-sensitive activities) or a long list (the same activities plus a sensitive one: visiting others during lockdown). Broadcast lists were blocked in groups of four, with random assignment across timing (treatment vs. control) and list length (short vs. long).\\n\\nData variables\\nResponseId: identifier for the responses from the individuals\\nBehavior: number of activities the respondent participated in, coded from 0 to 5\\nId: identifier for individuals participating\\nList: binary variable, equals 1 if the WhatsApp broadcast list is assigned to the long experimental list\\nqualtrics: binary variable indicating response mode (1 if Qualtrics, 0 if WhatsApp)\\nT: treatment indicator, equals 1 if the WhatsApp broadcast list is assigned to the treatment group\\nfemale: binary variable, equals 1 if the respondent is female\\nc_Q4: survey question on factual knowledge about COVID-19 transmission or prevention\\nc_Q5: survey question on factual knowledge about misinformation or fake cures\\nKnowledge: z-score of the respondent's answers to the factual questions\\nc_Q4_1: sub-item of Q4 (first option, coded 1 if correct, 0 otherwise)\\nc_Q4_3: sub-item of Q4 (third option, coded 1 if correct, 0 otherwise)\\nc_Q4_4: sub-item of Q4 (fourth option, coded 1 if correct, 0 otherwise)\\nc_Q4_5: sub-item of Q4 (fifth option, coded 1 if correct, 0 otherwise)\\nblock: randomization block (grouping of broadcast lists created at similar times)\\nlength_subscribe: number of months the WhatsApp broadcast list had been active\\nWeek: indicator for intervention week (1 = distancing message, 2 = misinformation/cures message)\\nresponse_rate: fraction of weekly responses coming from WhatsApp per person\\nWeek_id: numeric identifier for the week\\nurban: binary variable, equals 1 if the individual lived in an urban district\",\n \"columns\": {\n  \"Unnamed: 0\": \"int64\",\n  \"ResponseId\": \"str\",\n  \"Behavior\": \"float64\",\n  \"id\": \"int64\",\n  \"List\": \"int64\",\n  \"qualtrics\": \"int64\",\n  \"T\": \"int64\",\n  \"female\": \"int64\",\n  \"c_Q4\": \"float64\",\n  \"c_Q5\": \"float64\",\n  \"Knowledge\": \"float64\",\n  \"c_Q4_1\": \"float64\",\n  \"c_Q4_3\": \"float64\",\n  \"c_Q4_4\": \"float64\",\n  \"c_Q4_5\": \"float64\",\n  \"block\": \"int64\",\n  \"length_subscribe\": \"int64\",\n  \"Week\": \"int64\",\n  \"response_rate\": \"float64\",\n  \"Week_id\": \"int64\",\n  \"urban\": \"int64\"\n }\n}\n\n# Question\nWhat is the effect of WhatsApp messages on individuals's knowledge about Covid19?","response":"causcibench_metadata_rule/1: ols\ninfer__ols({\"dataset\": \"bowles_countering\", \"y\": \"Knowledge\", \"x\": [\"T\"], \"controls\": [\"qualtrics\", \"female\", \"urban\", \"Week\", \"block\"]})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_f05cc862","text":"causcibench_metadata_rule/1: ols","tool_calls":[{"vendor_id":"script_1","llm_name":"infer__ols","tool_id":"infer.ols","args":{"dataset":"bowles_countering","y":"Knowledge","x":["T"],"controls":["qualtrics","female","urban","Week","block"]}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"0e62848a10130d0514ca3be41f0a3809e185759e945b072ecd982495ba622a70","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["f65931e02c484d0eb25fd1d40c9323c893e1bc2a7a218aa31e38f197e62c09e3"],"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":0.7647909224033356}}
{"call_id":"call_b1d0bb8f","run_id":"20261015T171059Z-suite-causcibench_real-scripted-ded01d/causcibench_real.q174/e1a1","step":"analysis","kind":"tool","name":"infer.ols","version":"0.1.0","code_version":"2925aeab2262","inputs":{"dataset":"bowles_countering","y":"Knowledge","x":["T"],"controls":["qualtrics","female","urban","Week","block"],"fe":[],"cluster":null,"se":"HC1","weights":null,"subset":null,"intercept":true,"level":0.95},"input_digest":"6dc7b40413be99e3d110c6fc6156b70a8065e4e67508396e50995f91b9bf124c","dataset_digests":{"bowles_countering":"ce0afeaa1f0f9b2b7f6ca76a07d3288fa4145d73a3a3d4de3128c4e148972b63"},"result":{"tool":"infer.ols","version":"0.1.0","call_id":"call_b1d0bb8f","status":"ok","reason":"","figures":[{"key":"coef.T","value":0.25869400706938783,"unit":"","label":"Coefficient on T in the regression of Knowledge","se":0.06735672729050846,"ci_low":0.12649053743101865,"ci_high":0.390897476707757,"ci_level":0.95,"derivation":{"steps":[{"text_pt":"Coeficiente de T na regressão por mínimos quadrados ordinários de Knowledge.","formula":"","terms":[{"symbol":"n","value":864.0,"unit":"observações","note":""},{"symbol":"k","value":7.0,"unit":"parâmetros","note":""}],"result":0.25869400706938783,"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":0.004536928711287927,"unit":"","note":"diagonal da matriz de covariância"}],"result":0.06735672729050846,"unit":""},{"text_pt":"Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 857 graus de liberdade vezes o erro-padrão.","formula":"IC = β ± c · ep","terms":[{"symbol":"β","value":0.25869400706938783,"unit":"","note":""},{"symbol":"c","value":1.9627359427392872,"unit":"","note":"quantil 0.975"},{"symbol":"ep","value":0.06735672729050846,"unit":"","note":""}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"n_obs","value":864,"flag":false,"note":"observations used"},{"name":"k","value":7,"flag":false,"note":"parameters estimated, fixed-effect dummies included"},{"name":"r2","value":0.03650782802884589,"flag":false,"note":""},{"name":"df_resid","value":857,"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":0.060960360013029645,"se":0.15148911205304655,"t":0.40240753402583357,"p":0.6874844746774621,"ci_low":-0.2363727651471442,"ci_high":0.3582934851732035},{"term":"T","coef":0.25869400706938783,"se":0.06735672729050846,"t":3.840655825715,"p":0.00013174794794353436,"ci_low":0.12649053743101865,"ci_high":0.390897476707757},{"term":"qualtrics","coef":-0.3199935105334547,"se":0.08246624084184316,"t":-3.8802970435762947,"p":0.00011232965806124212,"ci_low":-0.48185296549633483,"ci_high":-0.15813405557057456},{"term":"female","coef":-0.06982169393800931,"se":0.06943918672355207,"t":-1.0055085209448098,"p":0.31493586262652384,"ci_low":-0.2061124815549097,"ci_high":0.06646909367889105},{"term":"urban","coef":-0.0704668294282691,"se":0.08022207220443374,"t":-0.8783970233116779,"p":0.37997449997829463,"ci_low":-0.2279215739449375,"ci_high":0.08698791508839931},{"term":"Week","coef":0.023347048191870473,"se":0.07084745032685735,"t":0.32953970939191174,"p":0.7418283095906059,"ci_low":-0.11570778901608872,"ci_high":0.16240188539982967},{"term":"block","coef":-0.0031363980813900816,"se":0.0037362412325097276,"t":-0.8394527778612635,"p":0.40144938136705777,"ci_low":-0.010469653039181459,"ci_high":0.004196856876401295}]},"datasets":{},"warnings":["4 rows were dropped because one of 'Knowledge', 'T', 'qualtrics', 'female', 'urban', 'Week', 'block' 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":"094613fd53ad6e0c52949962bd2fad24753ac482b0208fc405efb38707dd92bd","params_versions":[],"sources":[],"started_at":"2026-10-15T17:11:04.550871Z","duration_ms":4.8715840093791485,"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_f05cc862","llm_name":"infer__ols","vendor_id":"script_1","result_for_model_ref":"4bb4a25558e7ad5ebab412dbfa1cc7d4c580102cecfb12dbb3ac6fda6f917ec0"}}
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