{"call_id":"call_fe5f2d8a","run_id":"20261015T171142Z-suite-causcibench_real-naive-d6494d/causcibench_real.q106/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":"2026-10-15T17:11:44.788572Z","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\": \"enache_demand_for\",\n \"rows\": 14400,\n \"description\": \"The dataset consists of user-level information, such as total number of purchases and total videos watched, associated with five \\\"\\\"freemium\\\"\\\" mobile app games in six European markets for the years 2017-2022. The games are available on both iOS and Android platforms. However, only Apple changed its pricing tiers in between, as a result of which the prices of the games changed on the iOS platform.\\n\\nData Variables:\\ngame: Anonymized codenames of the games (W1-W5)\\ncountry: Two-letter country code (France, Germany, Great Britain, Italy, Netherlands, and Sweden)\\nplatform: Gaming platform (Android or ios)\\ndt: Date\\nnorm_users: Number of users normalized\\nnorm_purch_users: Normalized number of users who purchase premium\\nnorm_videos: Number of rewarded videos watched\\npurchsmooth: Number of purchases smoothed\\nyear: Year recorded\\nmonth: Month recorded (1-12)\\napple: Dummy variable for ios platform (1 = platform is ios, 0 = otherwise)\\ngoogle: Dummy variable for android platform (1 = platform is android, 0 = otherwise)\\npost: Indicator of the time period after price change (1 = post price change, 0 = pre price change)\\nwindow: Not applicable\\nuserssmooth: Number of users smoothed\\npostxapple: Interaction between post and apple\\nforpan: Concatenation of game codename, country, and platform\\ncgn: Concatenation of country and game codename\",\n \"columns\": {\n  \"game\": \"str\",\n  \"country\": \"str\",\n  \"platform\": \"str\",\n  \"dt\": \"str\",\n  \"norm_users\": \"float64\",\n  \"norm_purch_users\": \"float64\",\n  \"norm_videos\": \"float64\",\n  \"purchsmooth\": \"float64\",\n  \"year\": \"int64\",\n  \"month\": \"int64\",\n  \"apple\": \"float64\",\n  \"google\": \"float64\",\n  \"post\": \"float64\",\n  \"window\": \"float64\",\n  \"userssmooth\": \"float64\",\n  \"postxapple\": \"float64\",\n  \"forpan\": \"str\",\n  \"cgn\": \"str\"\n }\n}\n\n# Question\nDoes the number of users who purchase premium change after the price of premium is altered?","response":"Linha de base ingênua: regressão do resultado no tratamento, sem controles.\ninfer__ols({\"dataset\": \"enache_demand_for\", \"y\": \"norm_purch_users\", \"x\": [\"postxapple\"]})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_fe5f2d8a","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":"enache_demand_for","y":"norm_purch_users","x":["postxapple"]}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"709c28eb3e9f449d1021c0c4c02a30f82540b36f991ec043750b8ddc859d65ae","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["fe27170d795a536e10513d10c5cca59d2aba1e0c08e69f08cfa61e44ef181d61"],"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":0.868750037625432}}
{"call_id":"call_c7aafc3b","run_id":"20261015T171142Z-suite-causcibench_real-naive-d6494d/causcibench_real.q106/e1a1","step":"analysis","kind":"tool","name":"infer.ols","version":"0.1.0","code_version":"2925aeab2262","inputs":{"dataset":"enache_demand_for","y":"norm_purch_users","x":["postxapple"],"controls":[],"fe":[],"cluster":null,"se":"HC1","weights":null,"subset":null,"intercept":true,"level":0.95},"input_digest":"84cdb171dbdeaed2770635fbcf22a4f38a44bc49359fdfb1056195ad80729ce8","dataset_digests":{"enache_demand_for":"2b83258fea6a0022e7fea3285df6717f25a080e24b31c0fc50b042f3c75a9357"},"result":{"tool":"infer.ols","version":"0.1.0","call_id":"call_c7aafc3b","status":"ok","reason":"","figures":[{"key":"coef.postxapple","value":0.06876136482811339,"unit":"","label":"Coefficient on postxapple in the regression of norm_purch_users","se":0.01351808867740674,"ci_low":0.042263863178250184,"ci_high":0.0952588664779766,"ci_level":0.95,"derivation":{"steps":[{"text_pt":"Coeficiente de postxapple na regressão por mínimos quadrados ordinários de norm_purch_users.","formula":"","terms":[{"symbol":"n","value":12655.0,"unit":"observações","note":""},{"symbol":"k","value":2.0,"unit":"parâmetros","note":""}],"result":0.06876136482811339,"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":0.00018273872149023227,"unit":"","note":"diagonal da matriz de covariância"}],"result":0.01351808867740674,"unit":""},{"text_pt":"Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 12653 graus de liberdade vezes o erro-padrão.","formula":"IC = β ± c · ep","terms":[{"symbol":"β","value":0.06876136482811339,"unit":"","note":""},{"symbol":"c","value":1.960151489030355,"unit":"","note":"quantil 0.975"},{"symbol":"ep","value":0.01351808867740674,"unit":"","note":""}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"n_obs","value":12655,"flag":false,"note":"observations used"},{"name":"k","value":2,"flag":false,"note":"parameters estimated, fixed-effect dummies included"},{"name":"r2","value":0.002180139835853523,"flag":false,"note":""},{"name":"df_resid","value":12653,"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":1.063003845067275,"se":0.006488864638159044,"t":163.81969795086675,"p":0.0,"ci_low":1.0502846873846712,"ci_high":1.0757230027498788},{"term":"postxapple","coef":0.06876136482811339,"se":0.01351808867740674,"t":5.08661886077406,"p":3.6972188106446424e-7,"ci_low":0.042263863178250184,"ci_high":0.0952588664779766}]},"datasets":{},"warnings":["1745 rows were dropped because one of 'norm_purch_users', 'postxapple' 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":"0f020c1ccf88a68119e5a0811f5a0bea923bd74aac7aa48401a5bf0b357469e2","params_versions":[],"sources":[],"started_at":"2026-10-15T17:11:44.791150Z","duration_ms":3.720375010743737,"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_fe5f2d8a","llm_name":"infer__ols","vendor_id":"script_1","result_for_model_ref":"f2008b513f6d4d090cb72f8c9edd570c7f2c5a049f04f89bdb0ad957ce3e3c74"}}
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