{"call_id":"call_18935c05","run_id":"20261015T171122Z-suite-causcibench_real-baseline-4f971b/causcibench_real.q023/e1a1","step":"analysis","kind":"llm","name":"llm.analyst.baseline","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":"2026-10-15T17:11:23.180250Z","duration_ms":0.0,"error":null,"decided_by":"model","rationale":"","cached":false,"informed_by":[],"model":"rules-1","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\": \"lee_do_voters\",\n \"rows\": 13588,\n \"description\": \"This dataset is used to study whether elected officials moderate their policy positions in response to closely contested elections. The goal is to test whether policy outcomes reflect the preferences of a nearly divided electorate (convergence) or the ideology of the winning candidate (divergence). Legislative behavior is measured using the Americans for Democratic Action (ADA) score. For each congressional session, the ADA selects a set of key roll-call votes and assigns each representative a score from 0 to 100. Higher scores indicate a more liberal voting record.\\nThe dataset includes the following variables:\\n\\nstate: ICPSR state code identifying the state\\ndistrict: Congressional district number\\nid: Unique election identifier\\nyear: Year of the election\\nscore: ADA voting score (higher values indicate more liberal voting)\\ndemvoteshare: Share of votes received by the Democratic candidate (between 0 and 1)\\ndemocrat: Indicator equal to 1 if the Democrat won the election, i.e., secured more than 50% of the vote\\nlagdemvoteshare: Democratic vote share in the previous election (between 0 and 1)\\nlagdemocrat: Indicator equal to 1 if the Democrat won the previous election, i.e., secured more than 50% of the vote\",\n \"columns\": {\n  \"Unnamed: 0\": \"int64\",\n  \"state\": \"int64\",\n  \"district\": \"int64\",\n  \"id\": \"int64\",\n  \"score\": \"float64\",\n  \"year\": \"int64\",\n  \"demvoteshare\": \"float64\",\n  \"democrat\": \"int64\",\n  \"lagdemocrat\": \"int64\",\n  \"lagdemvoteshare\": \"float64\"\n }\n}\n\n# Question\nIf a Democrat won the previous congressional election in a district closely, is it likely that a Democrat will win the current election in that same district?","response":"O tratamento muda num limiar da variável de corte: regressão descontínua.\ninfer__rdd({\"dataset\": \"lee_do_voters\", \"y\": \"Unnamed: 0\", \"running\": \"score\", \"cutoff\": 0.0})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_18935c05","text":"O tratamento muda num limiar da variável de corte: regressão descontínua.","tool_calls":[{"vendor_id":"baseline_1","llm_name":"infer__rdd","tool_id":"infer.rdd","args":{"dataset":"lee_do_voters","y":"Unnamed: 0","running":"score","cutoff":0.0}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"c7690f89414d427bff9b2fc212dedeecf51d82a71f7e782dc524c070bbe17714","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["90160f9934d24ce56c05475fc05b57377b489f4987472cb0ddbe9770932ac403"],"config":{}},"attempts":[],"response_id":null,"finish_reason":"tool_use","model_reported":"rules-1","usage":{"input":0,"output":0,"thoughts":0,"cached":0},"thoughts":null,"vendor":false,"wall_ms":0.7963329553604126}}
{"call_id":"call_36a4f39b","run_id":"20261015T171122Z-suite-causcibench_real-baseline-4f971b/causcibench_real.q023/e1a1","step":"analysis","kind":"tool","name":"infer.rdd","version":"0.1.0","code_version":"2925aeab2262","inputs":{"dataset":"lee_do_voters","y":"Unnamed: 0","running":"score","cutoff":0.0,"bandwidth":null,"kernel":"triangular","polynomial":1,"cluster":null,"subset":null,"level":0.95},"input_digest":"88d54614aa3cc9eb47f48904a0eafe35cd395ec363c1ef29b7d04ee37fd9f6ef","dataset_digests":{"lee_do_voters":"4736dd24ef756d8688297bf924dd17c7367e5e6d2a5eb23cf41140965280d20b"},"result":{"tool":"infer.rdd","version":"0.1.0","call_id":"call_36a4f39b","status":"ok","reason":"","figures":[{"key":"effect","value":279.1647312977128,"unit":"","label":"Jump in Unnamed: 0 at the cutoff of score","se":248.25990671589517,"ci_low":-207.41574467071518,"ci_high":765.7452072661408,"ci_level":0.95,"derivation":{"steps":[{"text_pt":"Ajuste linear ponderado (kernel triangular) de cada lado do limiar, com janela h (Imbens–Kalyanaraman); o efeito é a diferença entre os dois ajustes no limiar.","formula":"","terms":[{"symbol":"h","value":14.20859737632235,"unit":"","note":"janela (Imbens–Kalyanaraman)"},{"symbol":"n_esq","value":758.0,"unit":"observações abaixo do limiar","note":""},{"symbol":"n_dir","value":3180.0,"unit":"observações acima do limiar","note":""}],"result":279.1647312977128,"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":61632.98128258497,"unit":"","note":"diagonal da matriz de covariância"}],"result":248.25990671589517,"unit":""},{"text_pt":"Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição normal vezes o erro-padrão.","formula":"IC = τ ± c · ep","terms":[{"symbol":"τ","value":279.1647312977128,"unit":"","note":""},{"symbol":"c","value":1.959963984540054,"unit":"","note":"quantil 0.975"},{"symbol":"ep","value":248.25990671589517,"unit":"","note":""}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"bandwidth","value":14.20859737632235,"flag":false,"note":"half-width of the window around the cutoff (Imbens–Kalyanaraman)"},{"name":"bandwidth_method","value":"IK","flag":false,"note":""},{"name":"n_left","value":758,"flag":false,"note":"observations with positive weight below the cutoff"},{"name":"n_right","value":3180,"flag":false,"note":"observations with positive weight at or above the cutoff"},{"name":"kernel","value":"triangular","flag":false,"note":""},{"name":"polynomial","value":1,"flag":false,"note":""},{"name":"bandwidth_sensitive","value":true,"flag":true,"note":"the estimate at half or double the bandwidth falls outside the main interval: the result depends on the window"},{"name":"n_obs","value":13588,"flag":false,"note":"rows used to choose the bandwidth"},{"name":"se_type","value":"HC1","flag":false,"note":"heteroskedasticity-robust standard errors (HC1)"}],"tables":{"sensitivity":[{"multiplier":0.5,"bandwidth":7.104298688161175,"estimate":-527.5201994302485,"se":358.2632343726077,"ci_low":-1229.7032357853918,"ci_high":174.66283692489503,"n_left":575,"n_right":1459},{"multiplier":1.0,"bandwidth":14.20859737632235,"estimate":279.1647312977128,"se":248.25990671589517,"ci_low":-207.41574467071518,"ci_high":765.7452072661408,"n_left":758,"n_right":3180},{"multiplier":2.0,"bandwidth":28.4171947526447,"estimate":487.3436579894352,"se":199.5709224780024,"ci_low":96.1918375711154,"ci_high":878.495478407755,"n_left":819,"n_right":5048}]},"datasets":{},"warnings":[],"assumptions":["units cannot precisely control the running variable around the cutoff","every other determinant of the outcome is continuous at the cutoff"],"notes":""},"outputs":null,"output_digest":"32ccb96ae17da5807516318cc4c2057cba4820008db99849a9c1d03ef1fb696d","params_versions":[],"sources":[],"started_at":"2026-10-15T17:11:23.182715Z","duration_ms":54.600541945546865,"error":null,"decided_by":"model","rationale":"O tratamento muda num limiar da variável de corte: regressão descontínua.","cached":false,"informed_by":[],"model":null,"prompt":null,"response":null,"tokens_in":null,"tokens_out":null,"meta":{"turn_id":"call_18935c05","llm_name":"infer__rdd","vendor_id":"baseline_1","result_for_model_ref":"09ef28ca1a7c313b0dfbc6def9e8d36a0e4346f1e8bd3712d2560552838f9d4b"}}
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