{"call_id":"call_0c3f3d46","run_id":"20261015T171142Z-suite-causcibench_real-naive-d6494d/causcibench_real.q160/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":"2026-10-15T17:11:46.926113Z","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\": \"sharkey_community\",\n \"rows\": 6043,\n \"description\": \"This study examines community organizations and public safety patterns across major US cities during a period of significant social change from 1990 to 2013. The dataset encompasses comprehensive information on 264 of the largest cities in the United States, capturing various aspects of urban life, including organizational presence, demographic characteristics, economic conditions, and public safety indicators. The analysis draws on multiple data sources, including the FBI's Uniform Crime Reports, the National Center for Charitable Statistics, and US Census data. The dataset represents one of the most comprehensive longitudinal examinations of urban organizational infrastructure and community safety patterns during a critical period in American urban development. Cities range from major metropolitan centers to smaller urban areas, providing broad geographic and demographic representation across all major US regions during the 1990s to 2010s period.\\n\\nVariable Descriptions:\\npoprank: Population-based ranking of cities within the national urban hierarchy\\nagency: Law enforcement agency responsible for crime reporting in each jurisdiction\\nplace_name: Official name of the municipal entity\\nplace_id: Unique numerical identifier for each city\\nplace_id_str: String version of the unique place identifier\\nstate_ab: Two-letter state postal abbreviation\\nstate_id: Numerical state identifier\\nstate_name: Full state name\\nregion: Census region classification code\\nregion_str: Census region name designation\\ndivision: US Census Bureau division classification\\nyear: Calendar year of observation (1990-2013)\\nyear_str: Year designation in string format\\nyears_crime: Total number of years for which crime data is available for each city\\nfirst_crime: Earliest year of available crime data\\nlast_crime: Most recent year of available crime data\\ny_1990 through y_2012: Binary indicators for each individual year in the dataset\\nyear_region: Combined year and region identifier\\nyear_region_num: Numerical encoding of year-region combinations\\nyear_reg_1 through year_reg_120: Individual year-region interaction indicators\\ntotpop: Total resident population count\\npop2010: Population count as recorded in the 2010 Census\\npop1990: Population count as recorded in the 1990 Census\\npop2000: Population count as recorded in the 2000 Census\\npopweights: Statistical weights based on population size\\narea: Overall geographic area of the municipal boundary\\npopdens: Population density per square unit of area\\nlogpop: Natural logarithm of the 1990 population\\nlogpopdens: Natural logarithm of population density\\nlargest50: Binary indicator for cities ranking among the 50 most populous in the US\\nlargest100: Binary indicator for cities ranking among the 100 most populous in the US\\nviol: Absolute count of violent crimes reported annually\\nviol_r: Violent crime rate per 100,000 residents\\nmurd: Absolute count of homicides reported annually\\nmurd_r: Murder rate per 100,000 residents\\nrape: Absolute count of reported sexual assaults\\nrape_r: Sexual assault rate per 100,000 residents\\nrobbery: Absolute count of reported robberies\\nrobbery_r: Robbery rate per 100,000 residents\\nassault: Absolute count of reported aggravated assaults\\nassault_r: Aggravated assault rate per 100,000 residents\\nprop: Absolute count of property crimes reported annually\\nprop_r: Property crime rate per 100,000 residents\\nburglary: Absolute count of reported burglaries\\nburglary_r: Burglary rate per 100,000 residents\\nlarceny: Absolute count of reported larceny-theft incidents\\nlarceny_r: Larceny-theft rate per 100,000 residents\\nautotheft: Absolute count of reported motor vehicle thefts\\nautotheft_r: Motor vehicle theft rate per 100,000 residents\\ncommunity_new: Count of newly established community-focused nonprofit organizations\\ncommunity_cml: Cumulative count of active community-focused nonprofit organizations\\ncrime_new: Count of newly established crime prevention organizations\\ncrime_cml: Cumulative count of active crime prevention organizations\\nhousing_new: Count of newly established housing-related organizations\\nhousing_cml: Cumulative count of active housing-related organizations\\njobs_new: Count of newly established workforce development organizations\\njobs_cml: Cumulative count of active workforce development organizations\\nnhood_new: Count of newly established neighborhood development organizations\\nnhood_cml: Cumulative count of active neighborhood development organizations\\nreentry_new: Count of newly established prisoner reentry support organizations\\nreentry_cml: Cumulative count of active prisoner reentry support organizations\\nsubstance_new: Count of newly established substance abuse prevention organizations\\nsubstance_cml: Cumulative count of active substance abuse prevention organizations\\nyouth_new: Count of newly established youth-related organizations\\nyouth_cml: Cumulative count of active youth-related organizations\\nVarious _r suffixed variables: Rate calculations expressing organizational counts per 100,000 residents (for example, substance_cml_r represents the cumulative count of substance-abuse prevention organizations per 100,000 residents)\\nall_new_r: Rate of all newly established nonprofits per 100,000 residents\\nall_cml_r: Rate of all cumulative active nonprofits per 100,000 residents\\nwhite: Percentage of residents identifying as white (non-Hispanic)\\nblack: Percentage of residents identifying as Black or African American\\nasian: Percentage of residents identifying as Asian\\nhispanic: Percentage of residents identifying as Hispanic or Latino\\nother: Percentage of residents identifying with other racial/ethnic categories\\nfborn: Percentage of foreign-born residents\\nmale1524: Percentage of male residents aged 15 to 24 years\\nfemale1524: Percentage of female residents aged 15 to 24 years\\nlesshs: Percentage of residents 25 years and older without a high school diploma\\ncollege: Percentage of residents 25 years and older with a bachelor's degree or higher\\nhsdropout: High school dropout rate\\nunemployed: Overall unemployment rate\\nunemployed_male: Male-specific unemployment rate\\nunemployed_female: Female-specific unemployment rate\\nnotlabforce: Percentage of residents not participating in the labor force\\nnotlabforce_male: Percentage of males not participating in the labor force\\nnotlabforce_female: Percentage of females not participating in the labor force\\nmanufacturing: Percentage of employed residents working in manufacturing industries\\npoverty: Percentage of residents living below the federal poverty line\\nvacancy: Housing vacancy rate\\nincarceration: Incarceration Rate\\nlog_incarceration: Natural logarithm of incarceration rate\\nswornftime_r: Number of sworn police officers per capita\\niv_cml: Cumulative number of new non-profits focusing on the arts, medical research, and environmental protectionism\\niv_cml_r: Cumulative number of new non-profits focusing on the arts, medical research, and environmental protectionism per 100,000 residents\\nlog_viol_r: Natural logarithm of violent crime rate\\nlog_prop_r: Natural logarithm of property crime rate\\nlog_murd_r: Natural logarithm of murder rate\\nMultiple lagged difference variables (log_viol_r_1ld through log_viol_r_5ld): Historical differences in log violent crime rates over 1-5 year periods\\nMultiple lagged growth variables (log_viol_r_1lg through log_viol_r_5lg): Historical growth patterns in log violent crime rates over 1-5 year periods; Similar lagged difference and growth variables for property crime (log_prop_r_Xld/lg) and murder rates (log_murd_r_Xld/lg)\",\n \"columns\": {\n  \"Unnamed: 0\": \"int64\",\n  \"poprank\": \"float64\",\n  \"agency\": \"str\",\n  \"place_name\": \"str\",\n  \"year\": \"float64\",\n  \"year_str\": \"int64\",\n  \"totpop\": \"float64\",\n  \"aland2010\": \"float64\",\n  \"state_ab\": \"str\",\n  \"place_id\": \"int64\",\n  \"years_crime\": \"int64\",\n  \"first_crime\": \"int64\",\n  \"last_crime\": \"int64\",\n  \"place_id_str\": \"int64\",\n  \"state_id\": \"float64\",\n  \"state_name\": \"str\",\n  \"pop2010\": \"int64\",\n  \"largest50\": \"float64\",\n  \"largest100\": \"float64\",\n  \"viol\": \"float64\",\n  \"viol_r\": \"float64\",\n  \"murd\": \"float64\",\n  \"murd_r\": \"float64\",\n  \"rape\": \"float64\",\n  \"rape_r\": \"float64\",\n  \"robbery\": \"float64\",\n  \"robbery_r\": \"float64\",\n  \"assault\": \"float64\",\n  \"assault_r\": \"float64\",\n  \"prop\": \"float64\",\n  \"prop_r\": \"float64\",\n  \"burglary\": \"float64\",\n  \"burglary_r\": \"float64\",\n  \"larceny\": \"float64\",\n  \"larceny_r\": \"float64\",\n  \"autotheft\": \"float64\",\n  \"autotheft_r\": \"float64\",\n  \"community_new\": \"float64\",\n  \"community_cml\": \"float64\",\n  \"crime_new\": \"float64\",\n  \"crime_cml\": \"float64\",\n  \"housing_new\": \"float64\",\n  \"housing_cml\": \"float64\",\n  \"iv1_new\": \"float64\",\n  \"iv1_cml\": \"float64\",\n  \"iv2_new\": \"float64\",\n  \"iv2_cml\": \"float64\",\n  \"jobs_new\": \"float64\",\n  \"jobs_cml\": \"float64\",\n  \"nhood_new\": \"float64\",\n  \"nhood_cml\": \"float64\",\n  \"reentry_new\": \"float64\",\n  \"reentry_cml\": \"float64\",\n  \"substance_new\": \"float64\",\n  \"substance_cml\": \"float64\",\n  \"youth_new\": \"float64\",\n  \"youth_cml\": \"float64\",\n  \"substance_new_r\": \"float64\",\n  \"substance_cml_r\": \"float64\",\n  \"crime_new_r\": \"float64\",\n  \"crime_cml_r\": \"float64\",\n  \"jobs_new_r\": \"float64\",\n  \"jobs_cml_r\": \"float64\",\n  \"youth_new_r\": \"float64\",\n  \"youth_cml_r\": \"float64\",\n  \"nhood_new_r\": \"float64\",\n  \"nhood_cml_r\": \"float64\",\n  \"iv1_new_r\": \"float64\",\n  \"iv1_cml_r\": \"float64\",\n  \"iv2_new_r\": \"float64\",\n  \"iv2_cml_r\": \"float64\",\n  \"all_new_r\": \"float64\",\n  \"all_cml_r\": \"float64\",\n  \"area\": \"float64\",\n  \"popdens\": \"float64\",\n  \"white\": \"float64\",\n  \"black\": \"float64\",\n  \"asian\": \"float64\",\n  \"hispanic\": \"float64\",\n  \"other\": \"float64\",\n  \"lesshs\": \"float64\",\n  \"college\": \"float64\",\n  \"hsdropout\": \"float64\",\n  \"unemployed\": \"float64\",\n  \"notlabforce\": \"float64\",\n  \"unemployed_male\": \"float64\",\n  \"notlabforce_male\": \"float64\",\n  \"unemployed_female\": \"float64\",\n  \"notlabforce_female\": \"float64\",\n  \"vacancy\": \"float64\",\n  \"fborn\": \"float64\",\n  \"male1524\": \"float64\",\n  \"female1524\": \"float64\",\n  \"manufacturing\": \"float64\",\n  \"poverty\": \"float64\",\n  \"region\": \"float64\",\n  \"region_str\": \"object\",\n  \"division\": \"float64\",\n  \"state\": \"str\",\n  \"incarceration\": \"float64\",\n  \"log_incarceraton\": \"float64\",\n  \"swornftime\": \"float64\",\n  \"swornftime_r\": \"float64\",\n  \"iv_cml_r\": \"float64\",\n  \"iv_new_r\": \"float64\",\n  \"log_viol_r\": \"float64\",\n  \"log_prop_r\": \"float64\",\n  \"log_murd_r\": \"float64\",\n  \"log_viol_r_5ld\": \"float64\",\n  \"log_viol_r_4ld\": \"float64\",\n  \"log_viol_r_3ld\": \"float64\",\n  \"log_viol_r_2ld\": \"float64\",\n  \"log_viol_r_1ld\": \"float64\",\n  \"log_viol_r_5lg\": \"float64\",\n  \"log_viol_r_4lg\": \"float64\",\n  \"log_viol_r_3lg\": \"float64\",\n  \"log_viol_r_2lg\": \"float64\",\n  \"log_viol_r_1lg\": \"float64\",\n  \"log_prop_r_5ld\": \"float64\",\n  \"log_prop_r_4ld\": \"float64\",\n  \"log_prop_r_3ld\": \"float64\",\n  \"log_prop_r_2ld\": \"float64\",\n  \"log_prop_r_1ld\": \"float64\",\n  \"log_prop_r_5lg\": \"float64\",\n  \"log_prop_r_4lg\": \"float64\",\n  \"log_prop_r_3lg\": \"float64\",\n  \"log_prop_r_2lg\": \"float64\",\n  \"log_prop_r_1lg\": \"float64\",\n  \"log_murd_r_5ld\": \"float64\",\n  \"log_murd_r_4ld\": \"float64\",\n  \"log_murd_r_3ld\": \"float64\",\n  \"log_murd_r_2ld\": \"float64\",\n  \"log_murd_r_1ld\": \"float64\",\n  \"log_murd_r_5lg\": \"float64\",\n  \"log_murd_r_4lg\": \"float64\",\n  \"log_murd_r_3lg\": \"float64\",\n  \"log_murd_r_2lg\": \"float64\",\n  \"log_murd_r_1lg\": \"float64\",\n  \"logpop\": \"float64\",\n  \"logpopdens\": \"float64\",\n  \"pop1990\": \"float64\",\n  \"pop2000\": \"float64\",\n  \"popweights\": \"float64\",\n  \"y_1990\": \"float64\",\n  \"y_1991\": \"float64\",\n  \"y_1992\": \"float64\",\n  \"y_1993\": \"float64\",\n  \"y_1994\": \"float64\",\n  \"y_1995\": \"float64\",\n  \"y_1996\": \"float64\",\n  \"y_1997\": \"float64\",\n  \"y_1998\": \"float64\",\n  \"y_1999\": \"float64\",\n  \"y_2000\": \"float64\",\n  \"y_2001\": \"float64\",\n  \"y_2002\": \"float64\",\n  \"y_2003\": \"float64\",\n  \"y_2004\": \"float64\",\n  \"y_2005\": \"float64\",\n  \"y_2006\": \"float64\",\n  \"y_2007\": \"float64\",\n  \"y_2008\": \"float64\",\n  \"y_2009\": \"float64\",\n  \"y_2010\": \"float64\",\n  \"y_2011\": \"float64\",\n  \"y_2012\": \"float64\",\n  \"year_region\": \"str\",\n  \"year_region_num\": \"str\",\n  \"year_reg_1\": \"int64\",\n  \"year_reg_2\": \"int64\",\n  \"year_reg_3\": \"int64\",\n  \"year_reg_4\": \"int64\",\n  \"year_reg_5\": \"int64\",\n  \"year_reg_6\": \"int64\",\n  \"year_reg_7\": \"int64\",\n  \"year_reg_8\": \"int64\",\n  \"year_reg_9\": \"int64\",\n  \"year_reg_10\": \"int64\",\n  \"year_reg_11\": \"int64\",\n  \"year_reg_12\": \"int64\",\n  \"year_reg_13\": \"int64\",\n  \"year_reg_14\": \"int64\",\n  \"year_reg_15\": \"int64\",\n  \"year_reg_16\": \"int64\",\n  \"year_reg_17\": \"int64\",\n  \"year_reg_18\": \"int64\",\n  \"year_reg_19\": \"int64\",\n  \"year_reg_20\": \"int64\",\n  \"year_reg_21\": \"int64\",\n  \"year_reg_22\": \"int64\",\n  \"year_reg_23\": \"int64\",\n  \"year_reg_24\": \"int64\",\n  \"year_reg_25\": \"int64\",\n  \"year_reg_26\": \"int64\",\n  \"year_reg_27\": \"int64\",\n  \"year_reg_28\": \"int64\",\n  \"year_reg_29\": \"int64\",\n  \"year_reg_30\": \"int64\",\n  \"year_reg_31\": \"int64\",\n  \"year_reg_32\": \"int64\",\n  \"year_reg_33\": \"int64\",\n  \"year_reg_34\": \"int64\",\n  \"year_reg_35\": \"int64\",\n  \"year_reg_36\": \"int64\",\n  \"year_reg_37\": \"int64\",\n  \"year_reg_38\": \"int64\",\n  \"year_reg_39\": \"int64\",\n  \"year_reg_40\": \"int64\",\n  \"year_reg_41\": \"int64\",\n  \"year_reg_42\": \"int64\",\n  \"year_reg_43\": \"int64\",\n  \"year_reg_44\": \"int64\",\n  \"year_reg_45\": \"int64\",\n  \"year_reg_46\": \"int64\",\n  \"year_reg_47\": \"int64\",\n  \"year_reg_48\": \"int64\",\n  \"year_reg_49\": \"int64\",\n  \"year_reg_50\": \"int64\",\n  \"year_reg_51\": \"int64\",\n  \"year_reg_52\": \"int64\",\n  \"year_reg_53\": \"int64\",\n  \"year_reg_54\": \"int64\",\n  \"year_reg_55\": \"int64\",\n  \"year_reg_56\": \"int64\",\n  \"year_reg_57\": \"int64\",\n  \"year_reg_58\": \"int64\",\n  \"year_reg_59\": \"int64\",\n  \"year_reg_60\": \"int64\",\n  \"year_reg_61\": \"int64\",\n  \"year_reg_62\": \"int64\",\n  \"year_reg_63\": \"int64\",\n  \"year_reg_64\": \"int64\",\n  \"year_reg_65\": \"int64\",\n  \"year_reg_66\": \"int64\",\n  \"year_reg_67\": \"int64\",\n  \"year_reg_68\": \"int64\",\n  \"year_reg_69\": \"int64\",\n  \"year_reg_70\": \"int64\",\n  \"year_reg_71\": \"int64\",\n  \"year_reg_72\": \"int64\",\n  \"year_reg_73\": \"int64\",\n  \"year_reg_74\": \"int64\",\n  \"year_reg_75\": \"int64\",\n  \"year_reg_76\": \"int64\",\n  \"year_reg_77\": \"int64\",\n  \"year_reg_78\": \"int64\",\n  \"year_reg_79\": \"int64\",\n  \"year_reg_80\": \"int64\",\n  \"year_reg_81\": \"int64\",\n  \"year_reg_82\": \"int64\",\n  \"year_reg_83\": \"int64\",\n  \"year_reg_84\": \"int64\",\n  \"year_reg_85\": \"int64\",\n  \"year_reg_86\": \"int64\",\n  \"year_reg_87\": \"int64\",\n  \"year_reg_88\": \"int64\",\n  \"year_reg_89\": \"int64\",\n  \"year_reg_90\": \"int64\",\n  \"year_reg_91\": \"int64\",\n  \"year_reg_92\": \"int64\",\n  \"year_reg_93\": \"int64\",\n  \"year_reg_94\": \"int64\",\n  \"year_reg_95\": \"int64\",\n  \"year_reg_96\": \"int64\",\n  \"year_reg_97\": \"int64\",\n  \"year_reg_98\": \"int64\",\n  \"year_reg_99\": \"int64\",\n  \"year_reg_100\": \"int64\",\n  \"year_reg_101\": \"int64\",\n  \"year_reg_102\": \"int64\",\n  \"year_reg_103\": \"int64\",\n  \"year_reg_104\": \"int64\",\n  \"year_reg_105\": \"int64\",\n  \"year_reg_106\": \"int64\",\n  \"year_reg_107\": \"int64\",\n  \"year_reg_108\": \"int64\",\n  \"year_reg_109\": \"int64\",\n  \"year_reg_110\": \"int64\",\n  \"year_reg_111\": \"int64\",\n  \"year_reg_112\": \"int64\",\n  \"year_reg_113\": \"int64\",\n  \"year_reg_114\": \"int64\",\n  \"year_reg_115\": \"int64\",\n  \"year_reg_116\": \"int64\",\n  \"year_reg_117\": \"int64\",\n  \"year_reg_118\": \"int64\",\n  \"year_reg_119\": \"int64\",\n  \"year_reg_120\": \"int64\",\n  \"np1\": \"float64\",\n  \"np2\": \"float64\",\n  \"npall\": \"float64\",\n  \"y\": \"float64\"\n }\n}\n\n# Question\nDoes local nonprofit communities have a causal effect on violent crime?","response":"Linha de base ingênua: regressão do resultado no tratamento, sem controles.\ninfer__ols({\"dataset\": \"sharkey_community\", \"y\": \"log_viol_r_1ld\", \"x\": [\"all_cml_r\"]})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_0c3f3d46","text":"Linha de base ingênua: regressão do resultado no tratamento, sem 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