{"call_id":"call_420468b9","run_id":"20261015T171142Z-suite-causcibench_real-naive-d6494d/causcibench_real.q153/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":"2026-10-15T17:11:46.571260Z","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\": \"hartford_deepiv\",\n \"rows\": 1000,\n \"description\": \"This dataset contains simulated consumer behavior data examining relationships between pricing decisions and market demand across various emotional and temporal contexts. The data structure reflects a complex economic environment where pricing strategies may be influenced by factors that also affect consumer demand. Pricing information is represented through both continuous normalized measures and categorical indicators distinguishing between different price levels. The data captures market demand responses across various pricing scenarios, with additional variables tracking price positioning within broader market distributions.\\n\\nVariable description:\\nobs_id: unique observation identifier\\nseed: randomization seed for simulation\\ntreatment_price_normalized: standardized pricing measure\\noutcome_demand: consumer demand response\\ninstrument_z: auxiliary variable for pricing analysis\\ntemporal_time: time measurement\\nstate_emotion_id: emotional state category identifier; takes values 0, 1, 2, ..., 6\\ncovariate_emotion_0 to covariate_emotion_6: emotional state indicators across seven categories\\nunobserved_confounder_v: unmeasured factor affecting pricing and demand\\nprice_percentile: pricing position within market distribution\\ntreatment_high_price: indicator for high pricing condition\\ntreatment_low_price: indicator for low pricing condition\\ninstrument_strong: strength indicator for auxiliary pricing variable\\ntime_category: categorical time period\\ncounterfactual_demand_low_price: hypothetical demand under low pricing\\ncounterfactual_demand_high_price: hypothetical demand under high pricing\\nindividual_treatment_effect: person-specific response to pricing changes\",\n \"columns\": {\n  \"obs_id\": \"int64\",\n  \"seed\": \"int64\",\n  \"treatment_price_normalized\": \"float64\",\n  \"outcome_demand\": \"float64\",\n  \"instrument_z\": \"float64\",\n  \"temporal_time\": \"float64\",\n  \"state_emotion_id\": \"int64\",\n  \"covariate_emotion_0\": \"float64\",\n  \"covariate_emotion_1\": \"float64\",\n  \"covariate_emotion_2\": \"float64\",\n  \"covariate_emotion_3\": \"float64\",\n  \"covariate_emotion_4\": \"float64\",\n  \"covariate_emotion_5\": \"float64\",\n  \"covariate_emotion_6\": \"float64\",\n  \"unobserved_confounder_v\": \"float64\",\n  \"price_percentile\": \"int64\",\n  \"treatment_high_price\": \"int64\",\n  \"treatment_low_price\": \"int64\",\n  \"instrument_strong\": \"int64\",\n  \"time_category\": \"str\",\n  \"counterfactual_demand_low_price\": \"float64\",\n  \"counterfactual_demand_high_price\": \"float64\",\n  \"individual_treatment_effect\": \"float64\"\n }\n}\n\n# Question\nHow does increase in product cost affect customer demand?","response":"Linha de base ingênua: regressão do resultado no tratamento, sem controles.\ninfer__ols({\"dataset\": \"hartford_deepiv\", \"y\": \"outcome_demand\", \"x\": [\"treatment_price_normalized\"]})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_420468b9","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":"hartford_deepiv","y":"outcome_demand","x":["treatment_price_normalized"]}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"a2be08c22418e5f99d74884496b58623f829b1cd34e8445acc19fc3a8299d1ac","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["871733d7a01e2f911192b7cae3b95f08c3cc79ec7026220c7a4f8d34180ea98e"],"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":0.5994578823447227}}
{"call_id":"call_77e36dd4","run_id":"20261015T171142Z-suite-causcibench_real-naive-d6494d/causcibench_real.q153/e1a1","step":"analysis","kind":"tool","name":"infer.ols","version":"0.1.0","code_version":"2925aeab2262","inputs":{"dataset":"hartford_deepiv","y":"outcome_demand","x":["treatment_price_normalized"],"controls":[],"fe":[],"cluster":null,"se":"HC1","weights":null,"subset":null,"intercept":true,"level":0.95},"input_digest":"14447f37406f7afa5990d9b277e1b0d3073a51ffd9bd95c379c96e08cec6995f","dataset_digests":{"hartford_deepiv":"83b4298ec803f1d6cfa4e643ce2340ea68a0ce366c6dc706ad7d6753904a0b69"},"result":{"tool":"infer.ols","version":"0.1.0","call_id":"call_77e36dd4","status":"ok","reason":"","figures":[{"key":"coef.treatment_price_normalized","value":0.30464356903547374,"unit":"","label":"Coefficient on treatment_price_normalized in the regression of outcome_demand","se":0.043298061890291485,"ci_low":0.21967788373196626,"ci_high":0.3896092543389812,"ci_level":0.95,"derivation":{"steps":[{"text_pt":"Coeficiente de treatment_price_normalized na regressão por mínimos quadrados ordinários de outcome_demand.","formula":"","terms":[{"symbol":"n","value":1000.0,"unit":"observações","note":""},{"symbol":"k","value":2.0,"unit":"parâmetros","note":""}],"result":0.30464356903547374,"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":0.0018747221634555118,"unit":"","note":"diagonal da matriz de covariância"}],"result":0.043298061890291485,"unit":""},{"text_pt":"Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 998 graus de liberdade vezes o erro-padrão.","formula":"IC = β ± c · ep","terms":[{"symbol":"β","value":0.30464356903547374,"unit":"","note":""},{"symbol":"c","value":1.9623438462163343,"unit":"","note":"quantil 0.975"},{"symbol":"ep","value":0.043298061890291485,"unit":"","note":""}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"n_obs","value":1000,"flag":false,"note":"observations used"},{"name":"k","value":2,"flag":false,"note":"parameters estimated, fixed-effect dummies included"},{"name":"r2","value":0.04446238294509708,"flag":false,"note":""},{"name":"df_resid","value":998,"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":-0.028023560216885797,"se":0.043902507186898254,"t":-0.6383134361230471,"p":0.5234161081602104,"ci_low":-0.11417537502856398,"ci_high":0.05812825459479238},{"term":"treatment_price_normalized","coef":0.30464356903547374,"se":0.043298061890291485,"t":7.035963175612313,"p":3.672459222635444e-12,"ci_low":0.21967788373196626,"ci_high":0.3896092543389812}]},"datasets":{},"warnings":[],"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":"9e8070ef7d72aa489e16c72918953785a0218d7a50700b5e12f870bed43424dd","params_versions":[],"sources":[],"started_at":"2026-10-15T17:11:46.574017Z","duration_ms":4.371042130514979,"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_420468b9","llm_name":"infer__ols","vendor_id":"script_1","result_for_model_ref":"9bdde8d5d873dd54281cd80e9351906e326c0d660c55cacc0eb62fa4d9ac10a1"}}
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