{"call_id":"call_d0946bb2","run_id":"20261015T171059Z-suite-causcibench_real-scripted-ded01d/causcibench_real.q158/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":"2026-10-15T17:11:04.098141Z","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\": \"butler_do_politicians\",\n \"rows\": 4859,\n \"description\": \"This study utilizes randomized field experiments to examine the interactions between constituents and state legislators across the United States during October 2008. Legislators across multiple states were sent an email asking for help with the voter registration process for the primary election using black (DeShawn Jackson) or white alias names (Jake Mueller). The sender name, the legislature receiving the email, and the (hypothetical) party affiliation of the sender were randomly assigned. Not all letters included information on which political party's primary election the sender was interested in. Legislative offices were contacted through official email addresses posted on state government websites. The outcome of interest is whether the legislator responded by the November election date or not.\\n\\nVariable Descriptions:\\nleg_party: Political party affiliation of the state legislator (R = Republican, D = Democrat)\\nleg_republican: Binary indicator for legislators affiliated with the Republican Party\\nleg_black: Binary indicator for legislators who identify as Black or African American\\nleg_latino: Binary indicator for legislators who identify as Latino or Hispanic\\nleg_white: Binary indicator for legislators who identify as white\\nleg_notwhite: Binary indicator for legislators who identify as non-white\\nleg_notblackotherminority: Binary indicator for legislators who identify as minorities other than Black (including Latino, Asian American, Native American, and Arab American legislators)\\nreply_atall: Binary indicator for whether the legislative office provided any form of response to the constituent inquiry\\ntreat_deshawn: Binary indicator for communications associated with the name \\\"\\\"DeShawn Jackson\\\"\\\"\\ntreat_jake: Binary indicator for communications associated with the name \\\"\\\"Jake Mueller\\\"\\\"\\ntreat_group: Categorical variable indicating the specific combination of communication characteristics\\ntreat_demprimary: Binary indicator for communications that mentioned interest in Democratic primary elections\\ntreat_repprimary: Binary indicator for communications that mentioned interest in Republican primary elections\\ntreat_noprimary: Binary indicator for communications that did not specify any particular primary election interest\\ntreat_primary: Binary indicator for communications that included any reference to primary election participation (either Democratic or Republican)\",\n \"columns\": {\n  \"leg_party\": \"str\",\n  \"leg_republican\": \"int64\",\n  \"leg_black\": \"int64\",\n  \"leg_latino\": \"int64\",\n  \"reply_atall\": \"int64\",\n  \"treat_deshawn\": \"int64\",\n  \"treat_demprimary\": \"int64\",\n  \"treat_repprimary\": \"int64\",\n  \"treat_noprimary\": \"int64\",\n  \"treat_group\": \"int64\",\n  \"treat_jake\": \"int64\",\n  \"leg_notwhite\": \"int64\",\n  \"leg_white\": \"int64\",\n  \"leg_notblackotherminority\": \"int64\",\n  \"treat_primary\": \"int64\"\n }\n}\n\n# Question\nDo legislators respond differently by race when a Democratic partisan signal is included?","response":"causcibench_metadata_rule/1: ols\ninfer__ols({\"dataset\": \"butler_do_politicians\", \"y\": \"reply_atall\", \"x\": [\"treat_deshawn\"], \"controls\": []})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_d0946bb2","text":"causcibench_metadata_rule/1: ols","tool_calls":[{"vendor_id":"script_1","llm_name":"infer__ols","tool_id":"infer.ols","args":{"dataset":"butler_do_politicians","y":"reply_atall","x":["treat_deshawn"],"controls":[]}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"8eab93b03c0182a166e05088d6e626811e93829a5ce0edf9668b2838d3075b4b","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["3221dba7d85bbeaa0c238d4ec5351a6c964cbca3abc4f43ed599ebf142053da7"],"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":0.7197919767349958}}
{"call_id":"call_4797fbce","run_id":"20261015T171059Z-suite-causcibench_real-scripted-ded01d/causcibench_real.q158/e1a1","step":"analysis","kind":"tool","name":"infer.ols","version":"0.1.0","code_version":"2925aeab2262","inputs":{"dataset":"butler_do_politicians","y":"reply_atall","x":["treat_deshawn"],"controls":[],"fe":[],"cluster":null,"se":"HC1","weights":null,"subset":null,"intercept":true,"level":0.95},"input_digest":"4443958e1eab762e1c8178973a5e6505c719a954223e5d2fc8516c953dfc5ea5","dataset_digests":{"butler_do_politicians":"0d32e2cb48fbabd40ab0ec59157724749089ca983ed6cba8ad1f10344a49f900"},"result":{"tool":"infer.ols","version":"0.1.0","call_id":"call_4797fbce","status":"ok","reason":"","figures":[{"key":"coef.treat_deshawn","value":-0.017824238945471624,"unit":"","label":"Coefficient on treat_deshawn in the regression of reply_atall","se":0.014223500324634713,"ci_low":-0.045708736104754594,"ci_high":0.010060258213811346,"ci_level":0.95,"derivation":{"steps":[{"text_pt":"Coeficiente de treat_deshawn na regressão por mínimos quadrados ordinários de reply_atall.","formula":"","terms":[{"symbol":"n","value":4859.0,"unit":"observações","note":""},{"symbol":"k","value":2.0,"unit":"parâmetros","note":""}],"result":-0.017824238945471624,"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":0.0002023079614848838,"unit":"","note":"diagonal da matriz de covariância"}],"result":0.014223500324634713,"unit":""},{"text_pt":"Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 4857 graus de liberdade vezes o erro-padrão.","formula":"IC = β ± c · ep","terms":[{"symbol":"β","value":-0.017824238945471624,"unit":"","note":""},{"symbol":"c","value":1.9604525273562785,"unit":"","note":"quantil 0.975"},{"symbol":"ep","value":0.014223500324634713,"unit":"","note":""}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"n_obs","value":4859,"flag":false,"note":"observations used"},{"name":"k","value":2,"flag":false,"note":"parameters estimated, fixed-effect dummies included"},{"name":"r2","value":0.00032322359701220105,"flag":false,"note":""},{"name":"df_resid","value":4857,"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":0.5742492801316331,"se":0.01003055222763491,"t":57.2500164596655,"p":0.0,"ci_low":0.5545848586661871,"ci_high":0.5939137015970791},{"term":"treat_deshawn","coef":-0.017824238945471624,"se":0.014223500324634713,"t":-1.253154184177894,"p":0.21020992334012087,"ci_low":-0.045708736104754594,"ci_high":0.010060258213811346}]},"datasets":{},"warnings":[],"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":"9958c146b00622ef14f30d0315633f7b533221895bd09a5ae97f63001736fc10","params_versions":[],"sources":[],"started_at":"2026-10-15T17:11:04.101195Z","duration_ms":2.5882080662995577,"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_d0946bb2","llm_name":"infer__ols","vendor_id":"script_1","result_for_model_ref":"1fe34c69a088776121e9be0ec7e77086f4fe46c82ee44e1b9e280a36b31a53dd"}}
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