{"call_id":"call_6810dd95","run_id":"20261015T171142Z-suite-causcibench_real-naive-d6494d/causcibench_real.q123/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":"2026-10-15T17:11:45.104819Z","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\": \"ruth_partisan_conflict\",\n \"rows\": 2240,\n \"description\": \"Social media companies have come under increasing pressure to remove misinformation from their platforms, but partisan disagreements over what should be removed have stymied efforts to deal with misinformation in the United States. This study aims to understand how party alignment can contribute to censorship opinions through randomized controlled trial. To test whether partisan conflict over content moderation may arise from the preference gap and party promotion, an experiment is embedded in a national survey of U.S. respondents. This survey was commissioned by the Knight Foundation and fielded by Ipsos in the summer of 2021. The survey was implemented on the Ipsos KnowledgePanel, which is described by Ipsos as a representative random sample \\n\\n- id: Participant ID\\n- accuracy_order: Order in which accuracy question appeared\\n- social_media_post_removed: Have you ever experienced the following? - Had a social media post removed by the social media company.\\n- social_media_post_flagged: Have you ever experienced the following? - Had a social media post flagged, reported, or tagged with a warning label.\\n- age: Age in years\\n- gender: Gender\\n- education: Education -- categorical\\n- household_income: Household Income\\n- race: Race\\n- weight: Survey weight\\n- hispanic: Hispanic ethnicity indicator\\n- party_id: Party identification\\n- party_id_dem: Democrat party ID indicator\\n- party_id_rep: Republican party ID indicator\\n- headline_order: Whether participants saw a headline in the first or second round\\n- remove: How do you think the social media company should handle this headline?\\n- censorship: Imagine that the social media platform removed this headline. Would you use the word “censorship” to describe this action?\\n- accuracy: To the best of your knowledge, how accurate is the claim in the above headline?\\n- harm: Some social media platforms allow users to report content as harmful. If you have the option of anonymously reporting this content as harmful, would you click the \\\"\\\"report as harmful content\\\"\\\" button for the above headline?\\n- headline: Headline that participants saw\\n- headline_orientation: Whether a headline is pro-Democrat or pro-Republican\\n- headline_pro_dem: Indicates whether a headline is favorable for Democrats or unfavorable for Republicans\\n- headline_pro_rep: Indicates whether a headline is favorable for Republicans or unfavorable for Democrats\\n- accuracy_binary: Indicates whether a headline was deemed more or less accurate\\n- social_media_most_common_newsformat: Indicates whether \\\"\\\"From social media\\\"\\\" was chosen in response to \\\"\\\"In which format do you get most of your news?\\\"\\\"\\n- political_interest: Political interest. Average of national politics, state politics, local politics, and international politics news consumption, each measured on a 4-point scale from 1 (Not at all closely) to 4 (Very closely)\\n- aligned: Indicates whether party ID and headline orientation are aligned (i.e., Democrat party ID and pro-Democrat headline, or Republican party ID and pro-Republican headline)\",\n \"columns\": {\n  \"id\": \"int64\",\n  \"accuracy_order\": \"int64\",\n  \"social_media_post_removed\": \"float64\",\n  \"social_media_post_flagged\": \"float64\",\n  \"age\": \"int64\",\n  \"gender\": \"int64\",\n  \"education\": \"int64\",\n  \"household_income\": \"int64\",\n  \"race\": \"str\",\n  \"weight\": \"float64\",\n  \"hispanic\": \"int64\",\n  \"party_id\": \"str\",\n  \"party_id_dem\": \"int64\",\n  \"party_id_rep\": \"int64\",\n  \"headline_order\": \"int64\",\n  \"remove\": \"float64\",\n  \"censorship\": \"float64\",\n  \"accuracy\": \"float64\",\n  \"harm\": \"float64\",\n  \"headline\": \"str\",\n  \"headline_orientation\": \"str\",\n  \"headline_pro_dem\": \"int64\",\n  \"headline_pro_rep\": \"int64\",\n  \"accuracy_binary\": \"float64\",\n  \"social_media_most_common_newsformat\": \"float64\",\n  \"political_interest\": \"float64\",\n  \"aligned\": \"int64\"\n }\n}\n\n# Question\nDoes the headline being aligned with a participant's party increase the likelihood that they support its removal?","response":"Linha de base ingênua: regressão do resultado no tratamento, sem controles.\ninfer__ols({\"dataset\": \"ruth_partisan_conflict\", \"y\": \"remove\", \"x\": [\"aligned\"]})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_6810dd95","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":"ruth_partisan_conflict","y":"remove","x":["aligned"]}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"4c192bcac674ea00fa35a69e45feea05765207df692f93453627efa92c5b81d3","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["3a1ee376b2579433066dcb9c3a7035d1a93e7d7bbba7301b8bb60a87166f5d52"],"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":0.8067500311881304}}
{"call_id":"call_9228992c","run_id":"20261015T171142Z-suite-causcibench_real-naive-d6494d/causcibench_real.q123/e1a1","step":"analysis","kind":"tool","name":"infer.ols","version":"0.1.0","code_version":"2925aeab2262","inputs":{"dataset":"ruth_partisan_conflict","y":"remove","x":["aligned"],"controls":[],"fe":[],"cluster":null,"se":"HC1","weights":null,"subset":null,"intercept":true,"level":0.95},"input_digest":"db8c9b2edf5df4ef2f15f848563694cbb66627cca21ebbe865be748cdf43cc64","dataset_digests":{"ruth_partisan_conflict":"b7761f8222289aca206c65f1cae91e4794bdc8f8f1ce43835b3fb16b695f4277"},"result":{"tool":"infer.ols","version":"0.1.0","call_id":"call_9228992c","status":"ok","reason":"","figures":[{"key":"coef.aligned","value":-0.06198541480404062,"unit":"","label":"Coefficient on aligned in the regression of remove","se":0.02116687686903927,"ci_low":-0.10349469314781647,"ci_high":-0.020476136460264777,"ci_level":0.95,"derivation":{"steps":[{"text_pt":"Coeficiente de aligned na regressão por mínimos quadrados ordinários de remove.","formula":"","terms":[{"symbol":"n","value":2190.0,"unit":"observações","note":""},{"symbol":"k","value":2.0,"unit":"parâmetros","note":""}],"result":-0.06198541480404062,"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":0.00044803667638906966,"unit":"","note":"diagonal da matriz de covariância"}],"result":0.02116687686903927,"unit":""},{"text_pt":"Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 2188 graus de liberdade vezes o erro-padrão.","formula":"IC = β ± c · ep","terms":[{"symbol":"β","value":-0.06198541480404062,"unit":"","note":""},{"symbol":"c","value":1.9610487933858276,"unit":"","note":"quantil 0.975"},{"symbol":"ep","value":0.02116687686903927,"unit":"","note":""}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"n_obs","value":2190,"flag":false,"note":"observations used"},{"name":"k","value":2,"flag":false,"note":"parameters estimated, fixed-effect dummies included"},{"name":"r2","value":0.00390419845664125,"flag":false,"note":""},{"name":"df_resid","value":2188,"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":0.593978102189781,"se":0.014840654930403766,"t":40.02371222666928,"p":2.5817080571196602e-263,"ci_low":0.5648748537454572,"ci_high":0.6230813506341047},{"term":"aligned","coef":-0.06198541480404062,"se":0.02116687686903927,"t":-2.928415712320154,"p":0.0034421472401531066,"ci_low":-0.10349469314781647,"ci_high":-0.020476136460264777}]},"datasets":{},"warnings":["50 rows were dropped because one of 'remove', 'aligned' 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":"01ce0a9252f2468c53ccb21cbbc2c5bdcc2e0d518c5261eeec86d05e30a8d4f2","params_versions":[],"sources":[],"started_at":"2026-10-15T17:11:45.107772Z","duration_ms":4.8422920517623425,"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_6810dd95","llm_name":"infer__ols","vendor_id":"script_1","result_for_model_ref":"67dd1419de91042c675cd87cab5799674d400985698146a0dfbc500b04ef060f"}}
{"call_id":"call_4000d23a","run_id":"20261015T171142Z-suite-causcibench_real-naive-d6494d/causcibench_real.q123/e1a1","step":"analysis","kind":"llm","name":"llm.analyst.scripted","version":"","code_version":"2925aeab2262","inputs":{},"input_digest":"","dataset_digests":{},"result":null,"outputs":null,"output_digest":"","params_versions":[],"sources":[],"started_at":"2026-10-15T17:11:45.112769Z","duration_ms":0.0,"error":null,"decided_by":"model","rationale":"","cached":false,"informed_by":[],"model":"naive-ols","prompt":null,"response":"submit_answer({\"answer\": \"Estimativa ingênua (infer.ols): coef.aligned = -0,06199.\", \"figure_refs\": [\"call_9228992c:coef.aligned\"], \"caveats\": []})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_4000d23a","text":"","tool_calls":[{"vendor_id":"script_2","llm_name":"submit_answer","tool_id":null,"args":{"answer":"Estimativa ingênua (infer.ols): coef.aligned = -0,06199.","figure_refs":["call_9228992c:coef.aligned"],"caveats":[]}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"4c192bcac674ea00fa35a69e45feea05765207df692f93453627efa92c5b81d3","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["3a1ee376b2579433066dcb9c3a7035d1a93e7d7bbba7301b8bb60a87166f5d52","4752e174209d4d4b59870ce44924d05d27cc04741fc21d534c7cf54203ecf895","4cc54bc1f04eab8186c8c465f773d0c7dd0a7fdd2eee11eec0f3a37db5d25e3a"],"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":0.29437500052154064}}
{"call_id":"call_97743814","run_id":"20261015T171142Z-suite-causcibench_real-naive-d6494d/causcibench_real.q123/e1a1","step":"analysis","kind":"note","name":"analyst.guard","version":"","code_version":"2925aeab2262","inputs":{},"input_digest":"","dataset_digests":{},"result":null,"outputs":null,"output_digest":"","params_versions":[],"sources":[],"started_at":"2026-10-15T17:11:45.120803Z","duration_ms":0.0,"error":null,"decided_by":"code","rationale":"","cached":false,"informed_by":[],"model":null,"prompt":null,"response":null,"tokens_in":null,"tokens_out":null,"meta":{"turn_id":"call_4000d23a","numbers_found":["-0,06199"],"unbacked":[],"uncited":[],"invalid_refs":[],"empty":false,"verdict":"accepted","retries_left":1,"vendor_id":"script_2"}}