{"call_id":"call_9b0f08fc","run_id":"20261015T171059Z-suite-causcibench_real-scripted-ded01d/causcibench_real.q098/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":"2026-10-15T17:11:01.513538Z","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\": \"song_estimating_incumbency\",\n \"rows\": 58153,\n \"description\": \"The dataset is a panel of electoral constituencies across multiple countries and years, designed to study incumbency effects. Each row corresponds to a party's performance in a given constituency at a given election.\\n\\nDataset Variables:\\nyear: The year of the election\\ncountry: The country where the election took place (e.g., Canada, Japan, UK, USA, Romania, etc.)\\noffice: Name of the office being contested (e.g., House of Commons, Mayor)\\ncst_n: Constituency identifier (e.g., \\\"\\\"abbotsford\\\"\\\", \\\"\\\"abitibi\\\"\\\")\\nl_vmargin: Lagged vote margin: the party's vote margin in the previous election (Values more than 0 indicate the party won (positive) and values less than 0 indicate the party lost (negative))\\nvmargin: Current vote margin: the party's vote margin in the current election\\nvs: Vote share of the party in the current election (between 0 and 1)\\nwin: Outcome of the current election: 1 means the party won the seat, 0 means the party lost\\nl_win: Lagged win indicator: 1 means the party won the previous election in this constituency, 0 means the party lost\\ninc: Incumbency indicator for the party's candidate in the current election: 1 means candidate is the incumbent, 0 means not incumbent, -1 appears to mark cases where incumbency is undefined or missing\\noffice_num: Encoded office type, combining country and office (e.g., Canada, House)\\nrd_s: Indicator variable that equals 1 if the observation is part of a subset of elections defined by narrow vote margins, and 0 otherwise\",\n \"columns\": {\n  \"Unnamed: 0\": \"int64\",\n  \"year\": \"int64\",\n  \"country\": \"str\",\n  \"office\": \"str\",\n  \"cst_n\": \"str\",\n  \"l_vmargin\": \"float64\",\n  \"vmargin\": \"float64\",\n  \"vs\": \"float64\",\n  \"win\": \"float64\",\n  \"l_win\": \"int64\",\n  \"inc\": \"int64\",\n  \"office_num\": \"str\",\n  \"rd_s\": \"int64\",\n  \"country_norm\": \"str\"\n }\n}\n\n# Question\nIn the UK, in very close elections, does just barely winning make it more likely that the party's candidate will stand again as the incumbent in the next election?","response":"causcibench_metadata_rule/1: rdd\ninfer__rdd({\"dataset\": \"song_estimating_incumbency\", \"y\": \"win\", \"running\": \"l_vmargin\", \"cutoff\": 0.0})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_9b0f08fc","text":"causcibench_metadata_rule/1: rdd","tool_calls":[{"vendor_id":"script_1","llm_name":"infer__rdd","tool_id":"infer.rdd","args":{"dataset":"song_estimating_incumbency","y":"win","running":"l_vmargin","cutoff":0.0}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"3ce935e36aa8b57cb98f8703f21bb7fd185fe562814b9285553abc600ecdc050","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["f110d684b338417fb758dcd3a9f3acb7257cfb261ca33395f244b1804772ccb7"],"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":2.0359999034553766}}
{"call_id":"call_878d39bc","run_id":"20261015T171059Z-suite-causcibench_real-scripted-ded01d/causcibench_real.q098/e1a1","step":"analysis","kind":"tool","name":"infer.rdd","version":"0.1.0","code_version":"2925aeab2262","inputs":{"dataset":"song_estimating_incumbency","y":"win","running":"l_vmargin","cutoff":0.0,"bandwidth":null,"kernel":"triangular","polynomial":1,"cluster":null,"subset":null,"level":0.95},"input_digest":"be598bd8d88ee1eef9dae98d5a5b99f01a19793c8528f572b1f2ad4e0ada1362","dataset_digests":{"song_estimating_incumbency":"b92d2fca9483051609676957c56c11b23d396f4044f39bece8e32f8049a0c08d"},"result":{"tool":"infer.rdd","version":"0.1.0","call_id":"call_878d39bc","status":"ok","reason":"","figures":[{"key":"effect","value":0.09871020361757638,"unit":"","label":"Jump in win at the cutoff of l_vmargin","se":0.016381752687952524,"ci_low":0.06660255834554721,"ci_high":0.13081784888960554,"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":0.1053167716797159,"unit":"","note":"janela (Imbens–Kalyanaraman)"},{"symbol":"n_esq","value":8046.0,"unit":"observações abaixo do limiar","note":""},{"symbol":"n_dir","value":8459.0,"unit":"observações acima do limiar","note":""}],"result":0.09871020361757638,"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":0.00026836182112923975,"unit":"","note":"diagonal da matriz de covariância"}],"result":0.016381752687952524,"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":0.09871020361757638,"unit":"","note":""},{"symbol":"c","value":1.959963984540054,"unit":"","note":"quantil 0.975"},{"symbol":"ep","value":0.016381752687952524,"unit":"","note":""}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"bandwidth","value":0.1053167716797159,"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":8046,"flag":false,"note":"observations with positive weight below the cutoff"},{"name":"n_right","value":8459,"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":false,"flag":false,"note":"the estimates at half and double the bandwidth fall inside the main interval"},{"name":"n_obs","value":57187,"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":0.05265838583985795,"estimate":0.09038203901052319,"se":0.02291386102049228,"ci_low":0.04547169666360212,"ci_high":0.13529238135744426,"n_left":4266,"n_right":4455},{"multiplier":1.0,"bandwidth":0.1053167716797159,"estimate":0.09871020361757638,"se":0.016381752687952524,"ci_low":0.06660255834554721,"ci_high":0.13081784888960554,"n_left":8046,"n_right":8459},{"multiplier":2.0,"bandwidth":0.2106335433594318,"estimate":0.1225507438280991,"se":0.011741315809541695,"ci_low":0.09953818771028664,"ci_high":0.14556329994591158,"n_left":14278,"n_right":14796}]},"datasets":{},"warnings":["966 rows were dropped because one of 'win', 'l_vmargin' was missing"],"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":"05340dba79f0593e9513708b7c6d30634b48a44535459bfaf074c86ab57f3a6b","params_versions":[],"sources":[],"started_at":"2026-10-15T17:11:01.517559Z","duration_ms":14.634833903983235,"error":null,"decided_by":"model","rationale":"causcibench_metadata_rule/1: rdd","cached":false,"informed_by":[],"model":null,"prompt":null,"response":null,"tokens_in":null,"tokens_out":null,"meta":{"turn_id":"call_9b0f08fc","llm_name":"infer__rdd","vendor_id":"script_1","result_for_model_ref":"b788cd36b0f15de5ea11474c937a09af6369aa8dcce678ce6255f3068932c659"}}
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