Tarefa causcibench_real.q170

Veredito

Pergunta feita ao analista

How does gaining legal access to alcohol at age 21 affect violent crime arrest rates?

Como o agente resolveu e como foi validado

Resumo passo a passo do que o agente executou, por que o caminho está correto e como cada verificação conferiu o resultado.

1. 1Preparação da base de dados
   carpenter_the_minimum_legal_drinking— This dataset contains comprehensive arrest and criminal justice records from California covering individuals around their 21st birthday. The data captures detailed information on various types of criminal offenses, demographic characteristics, and temporal variables that enable analysis of crime patterns across different age groups. The dataset includes both raw count data and population-adjusted rates for multiple categories of criminal activity, spanning from serious felonies to minor infractions and regulatory violations. Variable Descriptions: days_to_21: Number of days until or since an individual's 21st birthday years_to_21: Years until or since turning 21 all: Total number of arrests across all offense categories felony: Count of felony-level arrests misdemeanor: Count of misdemeanor-level arrests fbi_offense_miss: FBI-classified misdemeanor offenses violent: Total violent crime arrests murder: Homicide arrests manslaughter: Manslaughter arrests rape: Sexual assault arrests robbery: Robbery arrests assault: Total assault arrests aggravated_assault: Aggravated assault arrests ot_assault: Other types of assault property: Total property crime arrests burglary: Burglary arrests larceny: Theft arrests mv_theft: Motor vehicle theft arrests stolen_prop_buy_rec_poss: Receiving stolen property arrests vandalism: Property damage arrests ill_drugs: Total drug-related arrests cocaine_opio_sale_manuf: Cocaine/opiate sales and manufacturing arrests mj_sale_manuf: Marijuana sales and manufacturing arrests dang_non_narc_sale_manuf: Other dangerous drug sales/manufacturing arrests cocaine_opio_posses: Cocaine/opiate possession arrests mj_posses: Marijuana possession arrests dang_non_narc_posses: Other dangerous drug possession arrests alcohol: Total alcohol-related arrests drunk_at_risk: Public intoxication arrests dui: Driving under the influence arrests liquor_laws: Liquor law violation arrests drunkeness_pc: Public drunkenness arrests disorderly_cond: Disorderly conduct arrests vagrancy: Vagrancy arrests arson: Arson arrests forgery_counterfit: Forgery and counterfeiting arrests fraud: Fraud arrests embezelment: Embezzlement arrests weapons: Weapons violations prostitution: Prostitution arrests sex_offenses: Sexual offense arrests bookmaking: Illegal gambling arrests all_other_gambling: Other gambling violations aga_family_child: Offenses against family and children curfew_loitering: Curfew and loitering violations runaways: Runaway arrests coded_on_offense: Arrests coded by primary offense county_ordinance: County ordinance violations outside_warrent: Out-of-jurisdiction warrant arrests traffic_violations: Traffic violation arrests hit_run_reckl_driv: Hit and run/reckless driving arrests parole_violation: Parole violation arrests dump_or_litter: Illegal dumping/littering violations trespass: Trespassing arrests violate_court_order: Court order violations failure_to_appear: Failure to appear in court cont_delinq_minor: Contributing to the delinquency of a minor not_classified: Unclassified arrests other_reported: Other reported offenses pop1, pop2, pop3: Population counts for different demographic groups pop_day1, pop_day2, pop_day3: Daily population estimates post: Binary indicator variable that is 1 if age >= 21, i.e., the individual is 21 linear, square, cubic: years_to_21 raised to the power of 1, 2, and 3 respectively linear_post, square_post, cubic_post: Interaction of linear, square, cubic with post birthday_19, birthday_20, birthday_21, birthday_22, birthday_23: Dummy variables for 19th, 20th, 21st, 22nd, and 23rd birthdays birthday_19_1, birthday_20_1, birthday_21_1, birthday_22_1, birthday_23_1: Dummy variables for a day after the 19th, 20th, 21st, 22nd, and 23rd birthdays All variables with _r suffix represent population-adjusted rates corresponding to their count variable counterparts. These include crime-specific rates such as violent_r, property_r, alcohol_r, dui_r, and all_r, among others. The rate variables normalize the raw counts by relevant population denominators.
2. 2Chamada que gerou a resposta finalinfer.ols(Regressão linear por mínimos quadrados ordinários (MQO))citada na respostacoincide com a referência

   Ver chamada call_ef8ceb9b nos detalhes

   causcibench_metadata_rule/1: rdd

   Por que este é o caminho de referência: causcibench_metadata_rule/1: rdd
   Argumentos principais

   controls:

   ["days_to_21"]

   dataset:

   "carpenter_the_minimum_legal_drinking"

   intercept:

   true

   level:

   0,950.95

   se:

   "HC1"

   x:

   ["post"]

   y:

   "violent_r"

   Resultado calculado

   • coef.post = 11,0154 11.015396758768764
     Coefficient on post in the regression of violent_r
   Diagnósticos: n_obs = 2.9222922 (observations used) · k = 33 (parameters estimated, fixed-effect dummies included) · r2 = 0,5471790.547178896509784 · df_resid = 2.9192919 · se_type = "HC1" (heteroskedasticity-robust standard errors (HC1))
3. 3Conferência de lastro e resposta final

   Todos os 1 números escritos na resposta foram conferidos contra as ferramentas e o enunciado (nenhum número sem lastro).

   Números encontrados: 11,02
   Figuras citadas: call_ef8ceb9b:coef.post
   • ✓
     #0resposta com lastro

     every number in the answer was stated by a tool call
   • ✓
     #1figura da resposta próxima

     CauSciBench effect accuracy: |answer figure − gold| ≤ 5% of |gold|

Valores esperados e de onde vêm

Verificações

Caminho de referência e caminho do agente

A chamada de referência ao lado da chamada que deu a resposta, argumento por argumento.

Trajetória

Cada evento na ordem em que aconteceu: turnos do modelo, chamadas de ferramentas, guardas, transformações e pontuação.

1. 0Início

   How does gaining legal access to alcohol at age 21 affect violent crime arrest rates?

   carpenter_the_minimum_legal_drinking (agent)

   This dataset contains comprehensive arrest and criminal justice records from California covering individuals around their 21st birthday. The data captures detailed information on various types of criminal offenses, demographic characteristics, and temporal variables that enable analysis of crime patterns across different age groups. The dataset includes both raw count data and population-adjusted rates for multiple categories of criminal activity, spanning from serious felonies to minor infractions and regulatory violations. Variable Descriptions: days_to_21: Number of days until or since an individual's 21st birthday years_to_21: Years until or since turning 21 all: Total number of arrests across all offense categories felony: Count of felony-level arrests misdemeanor: Count of misdemeanor-level arrests fbi_offense_miss: FBI-classified misdemeanor offenses violent: Total violent crime arrests murder: Homicide arrests manslaughter: Manslaughter arrests rape: Sexual assault arrests robbery: Robbery arrests assault: Total assault arrests aggravated_assault: Aggravated assault arrests ot_assault: Other types of assault property: Total property crime arrests burglary: Burglary arrests larceny: Theft arrests mv_theft: Motor vehicle theft arrests stolen_prop_buy_rec_poss: Receiving stolen property arrests vandalism: Property damage arrests ill_drugs: Total drug-related arrests cocaine_opio_sale_manuf: Cocaine/opiate sales and manufacturing arrests mj_sale_manuf: Marijuana sales and manufacturing arrests dang_non_narc_sale_manuf: Other dangerous drug sales/manufacturing arrests cocaine_opio_posses: Cocaine/opiate possession arrests mj_posses: Marijuana possession arrests dang_non_narc_posses: Other dangerous drug possession arrests alcohol: Total alcohol-related arrests drunk_at_risk: Public intoxication arrests dui: Driving under the influence arrests liquor_laws: Liquor law violation arrests drunkeness_pc: Public drunkenness arrests disorderly_cond: Disorderly conduct arrests vagrancy: Vagrancy arrests arson: Arson arrests forgery_counterfit: Forgery and counterfeiting arrests fraud: Fraud arrests embezelment: Embezzlement arrests weapons: Weapons violations prostitution: Prostitution arrests sex_offenses: Sexual offense arrests bookmaking: Illegal gambling arrests all_other_gambling: Other gambling violations aga_family_child: Offenses against family and children curfew_loitering: Curfew and loitering violations runaways: Runaway arrests coded_on_offense: Arrests coded by primary offense county_ordinance: County ordinance violations outside_warrent: Out-of-jurisdiction warrant arrests traffic_violations: Traffic violation arrests hit_run_reckl_driv: Hit and run/reckless driving arrests parole_violation: Parole violation arrests dump_or_litter: Illegal dumping/littering violations trespass: Trespassing arrests violate_court_order: Court order violations failure_to_appear: Failure to appear in court cont_delinq_minor: Contributing to the delinquency of a minor not_classified: Unclassified arrests other_reported: Other reported offenses pop1, pop2, pop3: Population counts for different demographic groups pop_day1, pop_day2, pop_day3: Daily population estimates post: Binary indicator variable that is 1 if age >= 21, i.e., the individual is 21 linear, square, cubic: years_to_21 raised to the power of 1, 2, and 3 respectively linear_post, square_post, cubic_post: Interaction of linear, square, cubic with post birthday_19, birthday_20, birthday_21, birthday_22, birthday_23: Dummy variables for 19th, 20th, 21st, 22nd, and 23rd birthdays birthday_19_1, birthday_20_1, birthday_21_1, birthday_22_1, birthday_23_1: Dummy variables for a day after the 19th, 20th, 21st, 22nd, and 23rd birthdays All variables with _r suffix represent population-adjusted rates corresponding to their count variable counterparts. These include crime-specific rates such as violent_r, property_r, alcohol_r, dui_r, and all_r, among others. The rate variables normalize the raw counts by relevant population denominators.

   file 3083a3b3ad5ad5d822488e13fe0b49c98bcca2c9ab621e4fdec0abab049efd59 · frame —
2. 1Turno do modelo
   call_5c3720e4referenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

   Texto visível

   causcibench_metadata_rule/1: rdd

   Chamadas pedidas

   • infer.ols

     {
       "controls": [
         "days_to_21"
       ],
       "dataset": "carpenter_the_minimum_legal_drinking",
       "x": [
         "post"
       ],
       "y": "violent_r"
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
3. 2Ferramenta
   infer.ols— Regressão linear por mínimos quadrados ordinários (MQO)v0.1.0call_ef8ceb9bdecidido pelo modeloreexecutável

   Motivo: causcibench_metadata_rule/1: rdd

   Argumentos

   {
     "cluster": null,
     "controls": [
       "days_to_21"
     ],
     "dataset": "carpenter_the_minimum_legal_drinking",
     "fe": [],
     "intercept": true,
     "level": 0.95,
     "se": "HC1",
     "subset": null,
     "weights": null,
     "x": [
       "post"
     ],
     "y": "violent_r"
   }

   Figuras call_ef8ceb9b

   Chave	Valor	Valor bruto	Erro-padrão	IC
   coef.post Coefficient on post in the regression of violent_r	11,0154	11.015396758768764	0,5952770.595276935589955	9,84819 – 12,1826

   Diagnósticos

   • n_obs = 2.9222922 (observations used)
   • k = 33 (parameters estimated, fixed-effect dummies included)
   • r2 = 0,5471790.547178896509784
   • df_resid = 2.9192919
   • se_type = "HC1" (heteroskedasticity-robust standard errors (HC1))
   • n_clusters = — (errors not clustered)

   Derivação matemática e pressupostos

   coef.post

   1. Coeficiente de post na regressão por mínimos quadrados ordinários de violent_r.→ 11,0154 (11.015396758768764)
   2. Erro-padrão robusto à heterocedasticidade (HC1): a raiz da variância estimada do coeficiente.ep = √V_jj→ 0,595277 (0.595276935589955)
   3. Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 2919 graus de liberdade vezes o erro-padrão.IC = β ± c · ep

   Pressupostos do estimador

   • the conditional mean of the outcome is linear in the regressors
   • errors are independent across observations; their variance may differ

   Tabelas (1)

   {
     "coefficients": [
       {
         "ci_high": 149.75326547866032,
         "ci_low": 148.54892512603558,
         "coef": 149.15109530234795,
         "p": 0,
         "se": 0.30710793294527305,
         "t": 485.6634404456326,
         "term": "Intercept"
       },
       {
         "ci_high": 12.182602092154841,
         "ci_low": 9.848191425382687,
         "coef": 11.015396758768764,
         "p": 2.2336801002518576e-72,
         "se": 0.595276935589955,
         "t": 18.504659092581587,
         "term": "post"
       },
       {
         "ci_high": -0.014654557478015928,
         "ci_low": -0.016041649398384095,
         "coef": -0.015348103438200011,
         "p": 0,
         "se": 0.0003537097561673109,
         "t": -43.39180124548244,
         "term": "days_to_21"
       }
     ]
   }

   Hashes SHA-256 e metadados de reprodução

   Digests dos dados

   carpenter_the_minimum_legal_drinking c3e530c6e7505a6b45c809a4b7110c6ff1bcf4917d532cba2bd1fc079433bde4

   sha256 do resultado

   8327ad9fceb7a9675279b4dc350ca0f47ab4ae5e4cb1a99552d2532642716bd8

   Resultado mostrado ao modelo
4. 3Turno do modelo
   call_24822975referenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

   Chamadas pedidas

   • submit_answer (meta-ferramenta)

     {
       "answer": "Estimativa (infer.ols): coef.post = 11,02.",
       "caveats": [],
       "figure_refs": [
         "call_ef8ceb9b:coef.post"
       ]
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
5. 4Guarda
   resposta aceita

   Números encontrados

   11,02

   Sem lastro

   —

   Não citados

   —

   1 nova(s) tentativa(s) restante(s)
6. 5Resposta

   Estimativa (infer.ols): coef.post = 11,02.

   Figuras citadas: call_ef8ceb9b:coef.post · Parada: answered

   Figura da resposta: call_ef8ceb9b:coef.post = 11,015411.015396758768764 (se 0,5952770.595276935589955)
7. 6Pontuação
   #0resposta com lastropassouobrigatóriaevery number backed
8. 7Pontuação
   #1figura da resposta próximapassouobrigatória11.01539676 vs 10.876

Épocas × verificações

Tentativas

Cada tentativa é seu próprio arquivo e é publicada; só a última de cada época é pontuada.

• época 1, tentativa 1passoupontuada(exibindo)

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