{"call_id":"call_9540fb9c","run_id":"20261015T171142Z-suite-causcibench_real-naive-d6494d/causcibench_real.q133/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":"2026-10-15T17:11:45.569878Z","duration_ms":0.0,"error":null,"decided_by":"model","rationale":"","cached":false,"informed_by":[],"model":"naive-ols","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\": \"bokemper_experimental_evidence\",\n \"rows\": 3100,\n \"description\": \"The randomized experiment examines the causal relationship between beliefs, social norms, and reported intentions to engage in mask promoting behavior.\\nSubjects are randomly assigned to a control group, a \\\"\\\"Mask Protects You\\\"\\\" group, and a \\\"\\\"Mask Protects Others\\\"\\\" group.\\nThe control group reads a message unrelated to the experiment, the \\\"\\\"Mask Protects You\\\"\\\" group reads a message about how masks protect the wearer, and the \\\"\\\"Mask Protects Others\\\"\\\" group reads a message about how masks protect others.\\nAfter the treatment, subjects are asked about their beliefs about masks.\\nThey then read three vignette scenarios, taking place at an ATM, in a park or in a meeting in their neighborhood.\\nIn the first scenario, they are asked about what they would do when they have forgotten to bring their mask. In the second scenario, they are asked about what they would do if someone else was not wearing their mask properly. In the third scenario, they are asked about their evaluation of someone who took action against someone not wearing their mask properly.\\n\\nVariables in the dataset:\\n\\nage: age of the subject\\nfemale: whether the subject is female\\nlucid_hhi: household income\\nlucid_incmissing: whether the subject's income is missing\\nethnicity: ethnicity of the subject\\neducation: education level of the subject\\nworkstatus: work status of the subject\\nfluvaccine: whether the subject has received the flu vaccine\\npool_self_scenario_atm: whether the first vignette scenario takes place at an ATM\\npool_self_scenario_meet: whether the first vignette scenario takes place in a meeting in the neighborhood\\npool_other_scenario_atm: whether the second vignette scenario takes place at an ATM\\npool_other_scenario_meet: whether the second vignette scenario takes place in a meeting in the neighborhood\\npartisanship_1: whether the subject is a strong democrat\\npartisanship_2: whether the subject is a democrat\\npartisanship_3: whether the subject is a democrat lean\\npartisanship_4: whether the subject is an independent\\npartisanship_5: whether the subject is a republican lean\\npartisanship_6: whether the subject is a republican\\npartisanship_7: whether the subject is a strong republican\\nT_mask_protectyou: whether the subject was in the \\\"\\\"Mask Protects You\\\"\\\" group\\nT_mask_protectother: whether the subject was in the \\\"\\\"Mask Protects Others\\\"\\\" group\\nmskblfs_prtctothers: whether the subject believes that masks protect others after the intervention\\nmskblfs_prtctyou: whether the subject believes that masks protect the wearer after the intervention\\npool_self: own mask wearing behavior in the vignette scenario, 1 means they would continue the activity without a mask, 2 means they would continue the activity without a mask while keeping distance from others, 3 means they would abandon the activity, 4 means they would go retrieve their mask and then continue the activity\\npool_other: behavior when someone else is not wearing their mask properly, 1 means they would continue the activity as normal, 2 means they would continue the activity as normal while keeping distance from others, 3 means they would abandon the activity, 4 means they would ask the other person to wear the mask properly\\npool_self_others: whether others are wearing masks in the first vignette scenario\\npool_other_others: whether others are wearing masks in the second vignette scenario\",\n \"columns\": {\n  \"startdate\": \"str\",\n  \"enddate\": \"str\",\n  \"progress\": \"int64\",\n  \"durationinseconds\": \"int64\",\n  \"finished\": \"bool\",\n  \"recordeddate\": \"str\",\n  \"age\": \"float64\",\n  \"gender_3_text\": \"str\",\n  \"state\": \"str\",\n  \"device\": \"str\",\n  \"device_5_text\": \"str\",\n  \"typicalnews\": \"str\",\n  \"newscomp1\": \"str\",\n  \"newscomp2\": \"str\",\n  \"preexisting_you\": \"str\",\n  \"sdoutcomes_1\": \"str\",\n  \"sdoutcomes_2\": \"str\",\n  \"sdoutcomes_3\": \"str\",\n  \"sdoutcomes_4\": \"str\",\n  \"sdoutcomes_5\": \"str\",\n  \"sdoutcomes_6\": \"str\",\n  \"sdoutcomes_7\": \"str\",\n  \"sdoutcomes_8\": \"str\",\n  \"sdoutcomes_9\": \"str\",\n  \"sdoutcomes_10\": \"str\",\n  \"reportbusiness\": \"str\",\n  \"appuse\": \"str\",\n  \"fluvaccine\": \"int64\",\n  \"vaccinelikely_3month\": \"str\",\n  \"vaccinelikely_year\": \"str\",\n  \"localitydescribe\": \"str\",\n  \"localitydescribe_5_text\": \"str\",\n  \"housedescribe\": \"str\",\n  \"housedescribe_4_text\": \"str\",\n  \"privateentrance\": \"str\",\n  \"goingplaces\": \"str\",\n  \"marital\": \"str\",\n  \"numberinhome\": \"str\",\n  \"childreninhouse\": \"str\",\n  \"spouseeducation\": \"str\",\n  \"preexisting_house\": \"str\",\n  \"childmaskuse\": \"str\",\n  \"dem_emp_past\": \"str\",\n  \"dem_emp_past_9_text\": \"str\",\n  \"dem_emp_current\": \"str\",\n  \"dem_emp_current_9_text\": \"str\",\n  \"workingoutside\": \"str\",\n  \"workonlyfromhome\": \"str\",\n  \"religion\": \"str\",\n  \"religion_12_text\": \"str\",\n  \"religiousattendance\": \"str\",\n  \"politicalinterest\": \"str\",\n  \"ideology\": \"str\",\n  \"wtp_votecertainty\": \"str\",\n  \"wtp_exact\": \"str\",\n  \"trust_federal\": \"str\",\n  \"trust_state\": \"str\",\n  \"risktaking_1\": \"float64\",\n  \"presentbias\": \"str\",\n  \"timer_firstclick\": \"float64\",\n  \"timer_lastclick\": \"float64\",\n  \"timer_pagesubmit\": \"float64\",\n  \"timer_clickcount\": \"float64\",\n  \"atm3P_think\": \"str\",\n  \"park3P_think\": \"str\",\n  \"meet3P_think\": \"str\",\n  \"vote2016\": \"str\",\n  \"vote2016_3_text\": \"str\",\n  \"rid\": \"str\",\n  \"lucid_age\": \"int64\",\n  \"lucid_gender\": \"int64\",\n  \"lucid_hhi\": \"int64\",\n  \"lucid_ethnicity\": \"int64\",\n  \"lucid_hispanic\": \"int64\",\n  \"lucid_educ\": \"int64\",\n  \"lucid_region\": \"int64\",\n  \"lucid_zip\": \"str\",\n  \"political_party\": \"int64\",\n  \"name1\": \"str\",\n  \"name2\": \"str\",\n  \"masktreatment\": \"int64\",\n  \"gender1\": \"str\",\n  \"gender2\": \"str\",\n  \"gender1a\": \"str\",\n  \"gender1b\": \"str\",\n  \"gender2a\": \"str\",\n  \"atmtreatment\": \"int64\",\n  \"parktreatment\": \"int64\",\n  \"meetingtreatment\": \"int64\",\n  \"atmaction\": \"str\",\n  \"atmaction2\": \"str\",\n  \"parkaction\": \"str\",\n  \"parkaction2\": \"str\",\n  \"meetaction\": \"str\",\n  \"meetaction2\": \"str\",\n  \"maskjudgment\": \"int64\",\n  \"othersatmaction\": \"str\",\n  \"othersparkaction\": \"str\",\n  \"othersmeetaction\": \"str\",\n  \"comp1\": \"float64\",\n  \"comp2\": \"int64\",\n  \"compright\": \"int64\",\n  \"agegap\": \"float64\",\n  \"agediscrep\": \"int64\",\n  \"gender\": \"int64\",\n  \"genderdiscrep\": \"int64\",\n  \"female\": \"int64\",\n  \"lucid_incmissing\": \"int64\",\n  \"educ\": \"int64\",\n  \"education\": \"int64\",\n  \"lucid_party7\": \"int64\",\n  \"partisanship_1\": \"int64\",\n  \"partisanship_2\": \"int64\",\n  \"partisanship_3\": \"int64\",\n  \"partisanship_5\": \"int64\",\n  \"partisanship_6\": \"int64\",\n  \"partisanship_7\": \"int64\",\n  \"ethnicity\": \"float64\",\n  \"workstatus\": \"float64\",\n  \"libertyendorse_1\": \"float64\",\n  \"libertyendorse_2\": \"float64\",\n  \"libertyendorse_3\": \"float64\",\n  \"libertyendorse_4\": \"float64\",\n  \"libertyendorse_5\": \"float64\",\n  \"liberty_scale\": \"float64\",\n  \"atm3P_notmask_intel\": \"float64\",\n  \"atm3P_notmask_trust\": \"float64\",\n  \"atm3P_notmask_slfsh\": \"float64\",\n  \"atm3P_notmask_compt\": \"float64\",\n  \"atm3P_notmask_agrsv\": \"float64\",\n  \"atm3P_notmask_lkble\": \"float64\",\n  \"atm3P_notmask_rckls\": \"float64\",\n  \"atm3P_action_intel\": \"float64\",\n  \"atm3P_action_trust\": \"float64\",\n  \"atm3P_action_slfsh\": \"float64\",\n  \"atm3P_action_compt\": \"float64\",\n  \"atm3P_action_agrsv\": \"float64\",\n  \"atm3P_action_lkble\": \"float64\",\n  \"atm3P_action_rckls\": \"float64\",\n  \"park3P_notmask_intel\": \"float64\",\n  \"park3P_notmask_trust\": \"float64\",\n  \"park3P_notmask_slfsh\": \"float64\",\n  \"park3P_notmask_compt\": \"float64\",\n  \"park3P_notmask_agrsv\": \"float64\",\n  \"park3P_notmask_lkble\": \"float64\",\n  \"park3P_notmask_rckls\": \"float64\",\n  \"park3P_action_intel\": \"float64\",\n  \"park3P_action_trust\": \"float64\",\n  \"park3P_action_slfsh\": \"float64\",\n  \"park3P_action_compt\": \"float64\",\n  \"park3P_action_agrsv\": \"float64\",\n  \"park3P_action_lkble\": \"float64\",\n  \"park3P_action_rckls\": \"float64\",\n  \"meet3P_notmask_intel\": \"float64\",\n  \"meet3P_notmask_trust\": \"float64\",\n  \"meet3P_notmask_slfsh\": \"float64\",\n  \"meet3P_notmask_compt\": \"float64\",\n  \"meet3P_notmask_agrsv\": \"float64\",\n  \"meet3P_notmask_lkble\": \"float64\",\n  \"meet3P_notmask_rckls\": \"float64\",\n  \"meet3P_action_intel\": \"float64\",\n  \"meet3P_action_trust\": \"float64\",\n  \"meet3P_action_slfsh\": \"float64\",\n  \"meet3P_action_compt\": \"float64\",\n  \"meet3P_action_agrsv\": \"float64\",\n  \"meet3P_action_lkble\": \"float64\",\n  \"meet3P_action_rckls\": \"float64\",\n  \"mskblfs_prtctothers\": \"float64\",\n  \"mskblfs_prtctyou\": \"float64\",\n  \"binary_mskblfs_prtctothers\": \"float64\",\n  \"binary_mskblfs_prtctyou\": \"float64\",\n  \"T_mask_protectyou\": \"int64\",\n  \"T_mask_protectother\": \"int64\",\n  \"FirstScenario\": \"float64\",\n  \"atmself\": \"float64\",\n  \"atmother\": \"float64\",\n  \"T_thirdpartyatmaction\": \"float64\",\n  \"atm3P_notmask_slfsh_rc\": \"float64\",\n  \"atm3P_notmask_agrsv_rc\": \"float64\",\n  \"atm3P_notmask_rckls_rc\": \"float64\",\n  \"atm3P_negjudgescale\": \"float64\",\n  \"atm3P_action_slfsh_rc\": \"float64\",\n  \"atm3P_action_agrsv_rc\": \"float64\",\n  \"atm3P_action_rckls_rc\": \"float64\",\n  \"atm3P_actionnegjudge\": \"float64\",\n  \"T_atm_othersinline\": \"int64\",\n  \"parkself\": \"float64\",\n  \"parkother\": \"float64\",\n  \"T_thirdpartyparkaction\": \"float64\",\n  \"park3P_notmask_slfsh_rc\": \"float64\",\n  \"park3P_notmask_agrsv_rc\": \"float64\",\n  \"park3P_notmask_rckls_rc\": \"float64\",\n  \"park3P_negjudgescale\": \"float64\",\n  \"park3P_action_slfsh_rc\": \"float64\",\n  \"park3P_action_agrsv_rc\": \"float64\",\n  \"park3P_action_rckls_rc\": \"float64\",\n  \"park3P_actionnegjudge\": \"float64\",\n  \"T_park_othersatpark\": \"int64\",\n  \"meetself\": \"float64\",\n  \"meetother\": \"float64\",\n  \"T_thirdpartymeetaction\": \"float64\",\n  \"meet3P_notmask_slfsh_rc\": \"float64\",\n  \"meet3P_notmask_agrsv_rc\": \"float64\",\n  \"meet3P_notmask_rckls_rc\": \"float64\",\n  \"meet3P_negjudgescale\": \"float64\",\n  \"meet3P_action_slfsh_rc\": \"float64\",\n  \"meet3P_action_agrsv_rc\": \"float64\",\n  \"meet3P_action_rckls_rc\": \"float64\",\n  \"meet3P_actionnegjudge\": \"float64\",\n  \"T_meet_othersatmeet\": \"int64\",\n  \"atmself_binary\": \"float64\",\n  \"parkself_binary\": \"float64\",\n  \"meetself_binary\": \"float64\",\n  \"atmother_binary\": \"float64\",\n  \"parkother_binary\": \"float64\",\n  \"meetother_binary\": \"float64\",\n  \"pool_self\": \"float64\",\n  \"pool_self_binary\": \"float64\",\n  \"pool_self_others\": \"float64\",\n  \"pool_self_scenario_atm\": \"int64\",\n  \"pool_self_scenario_meet\": \"int64\",\n  \"pool_other\": \"float64\",\n  \"pool_other_binary\": \"float64\",\n  \"pool_other_others\": \"float64\",\n  \"pool_other_scenario_atm\": \"int64\",\n  \"pool_other_scenario_meet\": \"int64\",\n  \"pool_3Pnegjudge\": \"float64\",\n  \"pool_3Pothers\": \"int64\",\n  \"pool_3Paction\": \"int64\",\n  \"pool_3Pactionnegjudge\": \"float64\",\n  \"pool_3P_atm\": \"int64\",\n  \"pool_3P_meet\": \"int64\",\n  \"pool_3P_askfixmask\": \"int64\",\n  \"pool_3P_continueact\": \"int64\",\n  \"pool_self_alt_bin\": \"int64\",\n  \"sawtreatment\": \"int64\",\n  \"droppedout\": \"int64\",\n  \"yourmaskuse\": \"float64\",\n  \"othersmaskuse\": \"float64\"\n }\n}\n\n# Question\nHow much does greater mask use increase the likelihood that someone would encourage others to wear a mask?","response":"Linha de base ingênua: regressão do resultado no tratamento, sem controles.\ninfer__ols({\"dataset\": \"bokemper_experimental_evidence\", \"y\": \"pool_other\", \"x\": [\"pool_other_others\"]})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_9540fb9c","text":"Linha de base ingênua: regressão do resultado no tratamento, sem controles.","tool_calls":[{"vendor_id":"script_1","llm_name":"infer__ols","tool_id":"infer.ols","args":{"dataset":"bokemper_experimental_evidence","y":"pool_other","x":["pool_other_others"]}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"c7b7a7e4a343e723ba48cf64aea7f26bb6c2d1d0fea8d2ec3c2646421bd96b92","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["46b74a7bc24f5159483f2243bd27efbee5bd76b134d15ed7c80a70588860afe4"],"config":{}},"attempts":[],"response_id":null,"finish_reason":"tool_use","model_reported":"naive-ols","usage":{"input":0,"output":0,"thoughts":0,"cached":0},"thoughts":null,"vendor":false,"wall_ms":0.7771658711135387}}
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