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{"call_id":"call_c68374ab","run_id":"20261015T165744Z-suite-paper_repro-scripted-def9df/paper_repro.kessler_roth_2014.t18_2_twfe.design/e1a1","step":"analysis","kind":"llm","name":"llm.analyst.scripted","version":"","code_version":"62cdbe0860ad","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":"2026-10-15T16:57:56.376828Z","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\": \"organ_donations\",\n \"rows\": 162,\n \"description\": \"The organ donation panel of Kessler and Roth (2014) as distributed in the causaldata package and used in Huntington-Klein's The Effect, chapter 18: one row per U.S. state and quarter, from the fourth quarter of 2010 to the first quarter of 2012 (27 states, 6 quarters). California changed the organ-donor question on its driver's licence form to active-choice phrasing in July 2011 (the third quarter of 2011); the other states did not change theirs. Columns: rownames, the row number; State, the state's name; Quarter, the quarter as text (Q42010, Q12011, Q22011, Q32011, Q42011, Q12012); Quarter_Num, the quarter as a number from 1 (Q42010) to 6 (Q12012); Rate, the organ donor registration rate (the share of people registering as donors); and the book's construction of the treatment: California, 1 for California; After, 1 for the quarters after the change (Quarter_Num above 3: Q32011, Q42011 and Q12012); Treated, California times After.\",\n \"columns\": {\n  \"rownames\": \"int64\",\n  \"State\": \"str\",\n  \"Quarter\": \"str\",\n  \"Rate\": \"float64\",\n  \"Quarter_Num\": \"int64\",\n  \"California\": \"int64\",\n  \"After\": \"int64\",\n  \"Treated\": \"int64\"\n }\n}\n\n# Question\nDid California's switch to active-choice phrasing for the organ-donor question on its driver's licence form, in July 2011, change how many people registered as organ donors? Using the quarterly panel of U.S. states from the fourth quarter of 2010 to the first quarter of 2012, estimate the effect of the switch.","response":"The Effect, Table 18.2: two-way fixed effects regression of the donor registration rate on Treated with state and quarter fixed effects, errors clustered by state (feols(Rate ~ Treated | State + Quarter, vcov = ~State)).\ninfer__twfe({\"dataset\": \"organ_donations\", \"y\": \"Rate\", \"treat\": \"Treated\", \"unit\": \"State\", \"time\": \"Quarter_Num\"})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_c68374ab","text":"The Effect, Table 18.2: two-way fixed effects regression of the donor registration rate on Treated with state and quarter fixed effects, errors clustered by state (feols(Rate ~ Treated | State + Quarter, vcov = ~State)).","tool_calls":[{"vendor_id":"script_1","llm_name":"infer__twfe","tool_id":"infer.twfe","args":{"dataset":"organ_donations","y":"Rate","treat":"Treated","unit":"State","time":"Quarter_Num"}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"2ede085bf5963a76f4c893ddfbd779b7be0d414bc036f94f373e51a3c7f9e87e","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["bb2fea6a52d667105c5b06d48fa159922101bccf348e3f9b34d09bbafa2fb09a"],"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":0.7592909969389439}}
{"call_id":"call_e4b87bbd","run_id":"20261015T165744Z-suite-paper_repro-scripted-def9df/paper_repro.kessler_roth_2014.t18_2_twfe.design/e1a1","step":"analysis","kind":"tool","name":"infer.twfe","version":"0.1.0","code_version":"62cdbe0860ad","inputs":{"dataset":"organ_donations","y":"Rate","treat":"Treated","unit":"State","time":"Quarter_Num","covariates":[],"level":0.95},"input_digest":"4fd5c017af0ce240d570c2a1f1c9f4112c9206fd1625ec1cfbeac53c95c764ee","dataset_digests":{"organ_donations":"2c3e5c0fb1799ed60b282e41e0f6e4dda1b1a1f9704c871775e013e3a18c8c59"},"result":{"tool":"infer.twfe","version":"0.1.0","call_id":"call_e4b87bbd","status":"ok","reason":"","figures":[{"key":"coef.Treated","value":-0.022458974358974378,"unit":"","label":"TWFE coefficient on Treated for Rate","se":0.006015900729523345,"ci_low":-0.03424992312340837,"ci_high":-0.010668025594540385,"ci_level":0.95,"derivation":{"steps":[{"text_pt":"Coeficiente de Treated na regressão de Rate com efeitos fixos de State e de Quarter_Num (médias removidas por projeções alternadas).","formula":"","terms":[{"symbol":"n","value":162.0,"unit":"observações","note":""},{"symbol":"unidades","value":27.0,"unit":"","note":""},{"symbol":"períodos","value":6.0,"unit":"","note":""}],"result":-0.022458974358974378,"unit":""},{"text_pt":"Erro-padrão robusto por conglomerado (CR1): a raiz da variância estimada do coeficiente.","formula":"ep = √V_jj","terms":[{"symbol":"V_jj","value":0.000036191061587479514,"unit":"","note":"diagonal da matriz de covariância"}],"result":0.006015900729523345,"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 = β ± c · ep","terms":[{"symbol":"β","value":-0.022458974358974378,"unit":"","note":""},{"symbol":"c","value":1.959963984540054,"unit":"","note":"quantil 0.975"},{"symbol":"ep","value":0.006015900729523345,"unit":"","note":""}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"staggered","value":false,"flag":false,"note":"every treated unit adopts in the same period"},{"name":"n_adoption_dates","value":1,"flag":false,"note":""},{"name":"treatment_reversals","value":false,"flag":false,"note":""},{"name":"n_units","value":27,"flag":false,"note":""},{"name":"n_periods","value":6,"flag":false,"note":""},{"name":"n_obs","value":162,"flag":false,"note":""},{"name":"se_type","value":"cluster","flag":false,"note":"cluster-robust standard errors (CR1) by 'State'"},{"name":"n_clusters","value":27,"flag":true,"note":"27 clusters of 'State'; below about 40, cluster-robust errors are too small — confirm with infer.randomization_inference or a wild cluster bootstrap"}],"tables":{},"datasets":{},"warnings":[],"assumptions":["parallel trends across all units","the treatment effect is the same for every unit and period","errors are independent across clusters of 'State' and may be correlated within them"],"notes":""},"outputs":null,"output_digest":"29c0e79603156e25a8b65689c3feb10754b5ed39c0260a7104fe08342798b425","params_versions":[],"sources":[],"started_at":"2026-10-15T16:57:56.379828Z","duration_ms":13.386583887040615,"error":null,"decided_by":"model","rationale":"The Effect, Table 18.2: two-way fixed effects regression of the donor registration rate on Treated with state and quarter fixed effects, errors clustered by state (feols(Rate ~ Treated | State + Quarter, vcov = ~State)).","cached":false,"informed_by":[],"model":null,"prompt":null,"response":null,"tokens_in":null,"tokens_out":null,"meta":{"turn_id":"call_c68374ab","llm_name":"infer__twfe","vendor_id":"script_1","result_for_model_ref":"e033a8f50d9265becd92f7b25b0f95073193a6856fd68146c4bdc07d5f5db0b8"}}
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