{"call_id":"call_1e775a44","run_id":"20261015T171027Z-suite-crb_papers-naive-54c3f2/crb_papers.q087/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":"2026-10-15T17:10:29.889400Z","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\": \"ds_0056\",\n \"rows\": 32747,\n \"description\": \"| Variable Name   | Description                                                                                         |\\n|-----------------|-----------------------------------------------------------------------------------------------------|\\n| Unnamed: 0      | Row index carried over from the source file.                                                        |\\n| contact         | Indicator that the respondent (or someone in the household) reported contacting their U.S. House office. |\\n| rspartywon      | Indicator that the party the respondent identifies with currently holds the district’s House seat.  |\\n| rspartywinperc  | Vote-share margin for the respondent’s party in the most recent U.S. House general election (negative if loss). |\\n| distname        | Congressional district name (e.g., “StateNumber,” such as “Alabama1”).                             |\\n| distencoded     | Numeric code for the congressional district.                                                         |\\n| margin          | District vote margin from the most recent U.S. House election (same coding as rspartywinperc).      |\\n| pknowl          | Political knowledge score based on correct responses to a knowledge battery (scaled 0–1).           |\\n| church          | Self-reported frequency of attending religious services (scaled).                                    |\\n| churchmr        | Mean-replaced version of church (original missing values filled with the sample mean).              |\\n| churchmiss      | Missingness flag for church (1 if church was missing in the raw data, 0 otherwise).                 |\\n| dempidstr       | Strength of Democratic Party identification (scaled).                                                |\\n| dempidstrmr     | Mean-replaced version of dempidstr.                                                                  |\\n| dempidstrmiss   | Missingness flag for dempidstr.                                                                      |\\n| reppidstr       | Strength of Republican Party identification (scaled).                                                |\\n| reppidstrmr     | Mean-replaced version of reppidstr.                                                                  |\\n| reppidstrmiss   | Missingness flag for reppidstr.                                                                      |\\n| age             | Respondent age in years.                                                                             |\\n| agemr           | Mean-replaced version of age.                                                                        |\\n| actscale        | Composite scale of political activity/engagement (scaled).                                           |\\n| actscalemr      | Mean-replaced version of actscale.                                                                   |\\n| actscalemiss    | Missingness flag for actscale.                                                                       |\\n| followpol       | Self-reported attention to or following of politics (scaled).                                        |\\n| followpolmr     | Mean-replaced version of followpol.                                                                  |\\n| followpolmiss   | Missingness flag for followpol.                                                                      |\\n| incomecat       | Household income category (ordered category, scaled for analysis).                                   |\\n| incomecatmr     | Mean-replaced version of incomecat.                                                                  |\\n| incomecatmiss   | Missingness flag for incomecat.                                                                      |\\n| donategen       | Indicator for having made a political/campaign donation.                                             |\\n| donategenmr     | Mean-replaced version of donategen.                                                                  |\\n| donategenmiss   | Missingness flag for donategen.                                                                      |\\n\\nData Description: The dataset consists of respondent-level records from the 2008 Cooperative Congressional Election Study (CCES), fielded online by YouGov/Polimetrix in October–November 2008, matched to U.S. House district identifiers and district-level general election returns from the most recent contest at the time (the 2006 U.S. House elections, compiled from CQ Press). Each record links a survey respondent to their congressional district and includes whether they reported contacting their current U.S. House member’s office, their party identification strength, political knowledge, religious attendance, age, political engagement, attention to politics, income category, and whether they report having made political donations.\\n\\nTo provide district context, the file includes the district name/code and the party-aligned vote margin from the prior House election. Variables are typically scaled to 0–1 for analysis. For several measures the dataset includes a trio of columns: the original variable, a mean-replaced version used when handling missing survey responses, and a corresponding missingness indicator. The data support analyses of patterns in citizen communication with congressional offices alongside individual characteristics and district electoral context.\",\n \"columns\": {\n  \"Unnamed: 0\": \"int64\",\n  \"contact\": \"float64\",\n  \"rspartywon\": \"int64\",\n  \"rspartywinperc\": \"float64\",\n  \"distname\": \"str\",\n  \"distencoded\": \"int64\",\n  \"margin\": \"float64\",\n  \"pknowl\": \"float64\",\n  \"church\": \"float64\",\n  \"churchmr\": \"float64\",\n  \"churchmiss\": \"int64\",\n  \"dempidstr\": \"float64\",\n  \"dempidstrmr\": \"float64\",\n  \"dempidstrmiss\": \"int64\",\n  \"reppidstr\": \"float64\",\n  \"reppidstrmr\": \"float64\",\n  \"reppidstrmiss\": \"int64\",\n  \"age\": \"int64\",\n  \"agemr\": \"int64\",\n  \"actscale\": \"float64\",\n  \"actscalemr\": \"float64\",\n  \"actscalemiss\": \"int64\",\n  \"followpol\": \"float64\",\n  \"followpolmr\": \"float64\",\n  \"followpolmiss\": \"int64\",\n  \"incomecat\": \"float64\",\n  \"incomecatmr\": \"float64\",\n  \"incomecatmiss\": \"int64\",\n  \"donategen\": \"float64\",\n  \"donategenmr\": \"float64\",\n  \"donategenmiss\": \"int64\"\n }\n}\n\n# Question\nAmong voters in U.S. House districts decided at an infinitesimally small margin, what is the average effect of being represented by a copartisan, compared to an outpartisan, on the probability of reporting contact with the representative?","response":"Linha de base ingênua: regressão do resultado no tratamento, sem controles.\ninfer__ols({\"dataset\": \"ds_0056\", \"y\": \"contact\", \"x\": [\"rspartywon\"]})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_1e775a44","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":"ds_0056","y":"contact","x":["rspartywon"]}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"c930d083fecda30be70000614f2afa1f96d07e06c941410979d134e199ea7943","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["907dfdbd3f5517bb8d3f419b978f92458b9e09c304794cfa7ecf49bfba9edda6"],"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":0.7785831112414598}}
{"call_id":"call_b4784048","run_id":"20261015T171027Z-suite-crb_papers-naive-54c3f2/crb_papers.q087/e1a1","step":"analysis","kind":"tool","name":"infer.ols","version":"0.1.0","code_version":"2925aeab2262","inputs":{"dataset":"ds_0056","y":"contact","x":["rspartywon"],"controls":[],"fe":[],"cluster":null,"se":"HC1","weights":null,"subset":null,"intercept":true,"level":0.95},"input_digest":"76896d1f48a98664538cdbfe6f85f2e7866baef334140b3c5eb04bc99920d50a","dataset_digests":{"ds_0056":"d8648b0bc4e09bc117bf735e324619d9776db36fe1ab495db94aa5e863b4282f"},"result":{"tool":"infer.ols","version":"0.1.0","call_id":"call_b4784048","status":"ok","reason":"","figures":[{"key":"coef.rspartywon","value":0.023289715807587437,"unit":"","label":"Coefficient on rspartywon in the regression of contact","se":0.005531013379257855,"ci_low":0.012448726470041523,"ci_high":0.03413070514513335,"ci_level":0.95,"derivation":{"steps":[{"text_pt":"Coeficiente de rspartywon na regressão por mínimos quadrados ordinários de contact.","formula":"","terms":[{"symbol":"n","value":32617.0,"unit":"observações","note":""},{"symbol":"k","value":2.0,"unit":"parâmetros","note":""}],"result":0.023289715807587437,"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":0.000030592109001529395,"unit":"","note":"diagonal da matriz de covariância"}],"result":0.005531013379257855,"unit":""},{"text_pt":"Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 32615 graus de liberdade vezes o erro-padrão.","formula":"IC = β ± c · ep","terms":[{"symbol":"β","value":0.023289715807587437,"unit":"","note":""},{"symbol":"c","value":1.9600367227823532,"unit":"","note":"quantil 0.975"},{"symbol":"ep","value":0.005531013379257855,"unit":"","note":""}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"n_obs","value":32617,"flag":false,"note":"observations used"},{"name":"k","value":2,"flag":false,"note":"parameters estimated, fixed-effect dummies included"},{"name":"r2","value":0.0005380081180881291,"flag":false,"note":""},{"name":"df_resid","value":32615,"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":0.33484807983605097,"se":0.004454953393402552,"t":75.1630938119208,"p":0.0,"ci_low":0.3261162075866981,"ci_high":0.3435799520854038},{"term":"rspartywon","coef":0.023289715807587437,"se":0.005531013379257855,"t":4.210750220733045,"p":0.000025520588328049997,"ci_low":0.012448726470041523,"ci_high":0.03413070514513335}]},"datasets":{},"warnings":["130 rows were dropped because one of 'contact', 'rspartywon' 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":"f4843d0b23f5195738bd515015418bdc27f08dbf3f345ff9b68c70c4b6b46890","params_versions":[],"sources":[],"started_at":"2026-10-15T17:10:29.892363Z","duration_ms":18.964500166475773,"error":null,"decided_by":"model","rationale":"Linha de base ingênua: regressão do resultado no tratamento, sem controles.","cached":false,"informed_by":[],"model":null,"prompt":null,"response":null,"tokens_in":null,"tokens_out":null,"meta":{"turn_id":"call_1e775a44","llm_name":"infer__ols","vendor_id":"script_1","result_for_model_ref":"284cbbb246498096ce9f9e7d9c5133fa3f64436c014e424dd50bdd5e339dc607"}}
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