Tarefa causcibench_real.q054

Veredito

Pergunta feita ao analista

Does gaining legal access to alcohol at the U.S. minimum legal drinking age cause an increase in nonfatal morbidity?

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— This dataset aggregates near-census administrative records of emergency department visits and inpatient hospital admissions for U.S. young adults into age-in-months bins around the 21st birthday, with outcomes expressed as rates per 10,000 person-years by cause, gender, pregnancy exclusion, and contributing state tags where available. Age-running variables index months relative to 21, along with pre/post age polynomials and an indicator for the 21st-birthday month to accommodate birthday-month spikes documented in alcohol intoxication visits. Variables: months_21: Integer months relative to the 21st birthday (e.g., -24 to +24), indexing the running age variable used to organize age-specific rates age: Age reported in years corresponding to the same age-month cell, provided for convenience alongside months_21 bmonth: Indicator equal to 1 for the month containing the 21st birthday (the ""birthday month"" control used to accommodate celebration spikes), 0 otherwise post: Indicator equal to 1 for observations at or above age 21 in months (post-threshold), 0 otherwise linear, linear_post: Linear age terms for pre- and post-threshold segments, capturing smooth age trends on either side of age 21 within the specified bandwidth age_months: Age in months as a non-centered index (e.g., from 228 to 276), facilitating alternative specifications and diagnostics age_c, age_c_sq: Age in months centered at 21 (0 at 21) and its square for flexible smooth trends over: Synonym for post (equals 1 at and after 21), supplied for alternate coding conventions in estimation age_c_post, age_c_post_sq: Interactions of centered age and its square with the post indicator to allow different slopes/curvature after 21 dummy21: Binary flag for the 21st-birthday month, identical in purpose to bmonth for absorbing discrete celebration effects in that month visit_all_r: All non-pregnancy-screened ED visits per 10,000 person-years in the age-month cell alcohol_any_all_r: ED visits with primary alcohol intoxication coding per 10,000 injury_all_r: ED visits for any injury cause per 10,000 inj_by_self_all_r / inj_by_oth_all_r / inj_accident_all_r: ED visits per 10,000 for self-inflicted injuries, injuries inflicted by others, and accidental injuries, respectively illness_all_r: ED visits per 10,000 for residual illness (non-alcohol, non-injury) injury_or_alc_all_r: Composite of injury or alcohol intoxication ED visits per 10,000; visit_all_np_r, alcohol_any_all_np_r, injury_all_np_r, inj_by_self_all_np_r, inj_by_oth_all_np_r, inj_accident_all_np_r, illness_all_np_r, injury_or_alc_all_np_r: Same definitions as ""all_r"" but excluding pregnancy-related encounters, aligning with the study's analytic sample visit_m_r, alcohol_any_m_r, injury_m_r, inj_by_self_m_r, inj_by_oth_m_r, inj_accident_m_r, illness_m_r, injury_or_alc_m_r: Male-only ED rates per 10,000 by cause visit_f_r, alcohol_any_f_r, injury_f_r, inj_by_self_f_r, inj_by_oth_f_r, inj_accident_f_r, illness_f_r, injury_or_alc_f_r: Female-only ED rates per 10,000 by cause visit_f_np_r, alcohol_any_f_np_r, injury_f_np_r, inj_by_self_f_np_r, inj_by_oth_f_np_r, inj_accident_f_np_r, illness_f_np_r, injury_or_alc_f_np_r: Female-only non-pregnancy ED rates per 10,000 by cause visit_az_np_r, alcohol_any_az_np_r, injury_az_np_r, inj_by_self_az_np_r, inj_by_oth_az_np_r, inj_accident_az_np_r, illness_az_np_r, injury_or_alc_az_np_r: Arizona-specific ED rates per 10,000 (non-pregnancy), by cause visit_ca_np_r, alcohol_any_ca_np_r, injury_ca_np_r, inj_by_self_ca_np_r, inj_by_oth_ca_np_r, inj_accident_ca_np_r, illness_ca_np_r, injury_or_alc_ca_np_r: State-tagged ED rates provided where available in replication files, analogous definitions as above visit_ia_np_r, alcohol_any_ia_np_r, injury_ia_np_r, inj_by_self_ia_np_r, inj_by_oth_ia_np_r, inj_accident_ia_np_r, illness_ia_np_r, injury_or_alc_ia_np_r: State-tagged ED rates provided where available in replication files, analogous definitions as above visit_nj_np_r, alcohol_any_nj_np_r, injury_nj_np_r, inj_by_self_nj_np_r, inj_by_oth_nj_np_r, inj_accident_nj_np_r, illness_nj_np_r, injury_or_alc_nj_np_r: New Jersey-specific ED rates per 10,000 (non-pregnancy), by cause visit_wi_np_r, alcohol_any_wi_np_r, injury_wi_np_r, inj_by_self_wi_np_r, inj_by_oth_wi_np_r, inj_accident_wi_np_r, illness_wi_np_r, injury_or_alc_wi_np_r: Wisconsin-specific ED rates per 10,000 (non-pregnancy), by cause
2. 2Chamada que gerou a resposta finalinfer.ols(Regressão linear por mínimos quadrados ordinários (MQO))citada na respostacoincide com a referência

   Ver chamada call_51b8b40b nos detalhes

   causcibench_metadata_rule/1: rdd

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

   controls:

   ["months_21","dummy21","age_c","age_c_sq","age_c_post","age_c_post_sq"]

   dataset:

   "carpenter_the_minimum_legal"

   intercept:

   true

   level:

   0,950.95

   se:

   "HC1"

   x:

   ["over"]

   y:

   "visit_all_np_r"

   Resultado calculado

   • coef.over = 162,989 162.98920003605897
     Coefficient on over in the regression of visit_all_np_r
   Diagnósticos: n_obs = 8080 (observations used) · k = 77 (parameters estimated, fixed-effect dummies included) · r2 = 0,892510.8925098904625572 · df_resid = 7373 · 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: 163,0
   Figuras citadas: call_51b8b40b:coef.over
   • ✓
     #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

   Does gaining legal access to alcohol at the U.S. minimum legal drinking age cause an increase in nonfatal morbidity?

   carpenter_the_minimum_legal (agent)

   This dataset aggregates near-census administrative records of emergency department visits and inpatient hospital admissions for U.S. young adults into age-in-months bins around the 21st birthday, with outcomes expressed as rates per 10,000 person-years by cause, gender, pregnancy exclusion, and contributing state tags where available. Age-running variables index months relative to 21, along with pre/post age polynomials and an indicator for the 21st-birthday month to accommodate birthday-month spikes documented in alcohol intoxication visits. Variables: months_21: Integer months relative to the 21st birthday (e.g., -24 to +24), indexing the running age variable used to organize age-specific rates age: Age reported in years corresponding to the same age-month cell, provided for convenience alongside months_21 bmonth: Indicator equal to 1 for the month containing the 21st birthday (the ""birthday month"" control used to accommodate celebration spikes), 0 otherwise post: Indicator equal to 1 for observations at or above age 21 in months (post-threshold), 0 otherwise linear, linear_post: Linear age terms for pre- and post-threshold segments, capturing smooth age trends on either side of age 21 within the specified bandwidth age_months: Age in months as a non-centered index (e.g., from 228 to 276), facilitating alternative specifications and diagnostics age_c, age_c_sq: Age in months centered at 21 (0 at 21) and its square for flexible smooth trends over: Synonym for post (equals 1 at and after 21), supplied for alternate coding conventions in estimation age_c_post, age_c_post_sq: Interactions of centered age and its square with the post indicator to allow different slopes/curvature after 21 dummy21: Binary flag for the 21st-birthday month, identical in purpose to bmonth for absorbing discrete celebration effects in that month visit_all_r: All non-pregnancy-screened ED visits per 10,000 person-years in the age-month cell alcohol_any_all_r: ED visits with primary alcohol intoxication coding per 10,000 injury_all_r: ED visits for any injury cause per 10,000 inj_by_self_all_r / inj_by_oth_all_r / inj_accident_all_r: ED visits per 10,000 for self-inflicted injuries, injuries inflicted by others, and accidental injuries, respectively illness_all_r: ED visits per 10,000 for residual illness (non-alcohol, non-injury) injury_or_alc_all_r: Composite of injury or alcohol intoxication ED visits per 10,000; visit_all_np_r, alcohol_any_all_np_r, injury_all_np_r, inj_by_self_all_np_r, inj_by_oth_all_np_r, inj_accident_all_np_r, illness_all_np_r, injury_or_alc_all_np_r: Same definitions as ""all_r"" but excluding pregnancy-related encounters, aligning with the study's analytic sample visit_m_r, alcohol_any_m_r, injury_m_r, inj_by_self_m_r, inj_by_oth_m_r, inj_accident_m_r, illness_m_r, injury_or_alc_m_r: Male-only ED rates per 10,000 by cause visit_f_r, alcohol_any_f_r, injury_f_r, inj_by_self_f_r, inj_by_oth_f_r, inj_accident_f_r, illness_f_r, injury_or_alc_f_r: Female-only ED rates per 10,000 by cause visit_f_np_r, alcohol_any_f_np_r, injury_f_np_r, inj_by_self_f_np_r, inj_by_oth_f_np_r, inj_accident_f_np_r, illness_f_np_r, injury_or_alc_f_np_r: Female-only non-pregnancy ED rates per 10,000 by cause visit_az_np_r, alcohol_any_az_np_r, injury_az_np_r, inj_by_self_az_np_r, inj_by_oth_az_np_r, inj_accident_az_np_r, illness_az_np_r, injury_or_alc_az_np_r: Arizona-specific ED rates per 10,000 (non-pregnancy), by cause visit_ca_np_r, alcohol_any_ca_np_r, injury_ca_np_r, inj_by_self_ca_np_r, inj_by_oth_ca_np_r, inj_accident_ca_np_r, illness_ca_np_r, injury_or_alc_ca_np_r: State-tagged ED rates provided where available in replication files, analogous definitions as above visit_ia_np_r, alcohol_any_ia_np_r, injury_ia_np_r, inj_by_self_ia_np_r, inj_by_oth_ia_np_r, inj_accident_ia_np_r, illness_ia_np_r, injury_or_alc_ia_np_r: State-tagged ED rates provided where available in replication files, analogous definitions as above visit_nj_np_r, alcohol_any_nj_np_r, injury_nj_np_r, inj_by_self_nj_np_r, inj_by_oth_nj_np_r, inj_accident_nj_np_r, illness_nj_np_r, injury_or_alc_nj_np_r: New Jersey-specific ED rates per 10,000 (non-pregnancy), by cause visit_wi_np_r, alcohol_any_wi_np_r, injury_wi_np_r, inj_by_self_wi_np_r, inj_by_oth_wi_np_r, inj_accident_wi_np_r, illness_wi_np_r, injury_or_alc_wi_np_r: Wisconsin-specific ED rates per 10,000 (non-pregnancy), by cause

   file e49a7ad686067a4e4e119b7ab85a0dc1a8e562b1b5fc83d3f4e1d9b4b626d6e4 · frame —
2. 1Turno do modelo
   call_098129b0referenceFim: 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": [
         "months_21",
         "dummy21",
         "age_c",
         "age_c_sq",
         "age_c_post",
         "age_c_post_sq"
       ],
       "dataset": "carpenter_the_minimum_legal",
       "x": [
         "over"
       ],
       "y": "visit_all_np_r"
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
3. 2Ferramenta
   infer.ols— Regressão linear por mínimos quadrados ordinários (MQO)v0.1.0call_51b8b40bdecidido pelo modeloreexecutável

   Motivo: causcibench_metadata_rule/1: rdd

   Argumentos

   {
     "cluster": null,
     "controls": [
       "months_21",
       "dummy21",
       "age_c",
       "age_c_sq",
       "age_c_post",
       "age_c_post_sq"
     ],
     "dataset": "carpenter_the_minimum_legal",
     "fe": [],
     "intercept": true,
     "level": 0.95,
     "se": "HC1",
     "subset": null,
     "weights": null,
     "x": [
       "over"
     ],
     "y": "visit_all_np_r"
   }

   Figuras call_51b8b40b

   Chave	Valor	Valor bruto	Erro-padrão	IC
   coef.over Coefficient on over in the regression of visit_all_np_r	162,989	162.98920003605897	30,333530.333516051790983	102,535 – 223,444

   Diagnósticos

   • n_obs = 8080 (observations used)
   • k = 77 (parameters estimated, fixed-effect dummies included)
   • r2 = 0,892510.8925098904625572
   • df_resid = 7373
   • se_type = "HC1" (heteroskedasticity-robust standard errors (HC1))
   • n_clusters = — (errors not clustered)

   Derivação matemática e pressupostos

   coef.over

   1. Coeficiente de over na regressão por mínimos quadrados ordinários de visit_all_np_r.→ 162,989 (162.98920003605897)
   2. Erro-padrão robusto à heterocedasticidade (HC1): a raiz da variância estimada do coeficiente.ep = √V_jj→ 30,3335 (30.333516051790983)
   3. Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 73 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": 3948.652903863484,
         "ci_low": 3831.6578002135006,
         "coef": 3890.1553520384923,
         "p": 9.653614663231278e-89,
         "se": 29.351548512079717,
         "t": 132.53663091872264,
         "term": "Intercept"
       },
       {
         "ci_high": 223.44381034541217,
         "ci_low": 102.53458972670578,
         "coef": 162.98920003605897,
         "p": 8.837174535120515e-7,
         "se": 30.333516051790983,
         "t": 5.37323796416392,
         "term": "over"
       },
       {
         "ci_high": -5.49641613631155,
         "ci_low": -20.890472380137147,
         "coef": -13.193444258224348,
         "p": 0.0010409058489956563,
         "se": 3.8620367395041506,
         "t": -3.4161881794833113,
         "term": "months_21"
       },
       {
         "ci_high": 41.158618537673306,
         "ci_low": 10.643288741769826,
         "coef": 25.900953639721564,
         "p": 0.0011547969909556186,
         "se": 7.655638184194235,
         "t": 3.383252057705189,
         "term": "dummy21"
       },
       {
         "ci_high": -65.34330930052423,
         "ci_low": -124.10174323695361,
         "coef": -94.72252626873892,
         "p": 1.1863728040408033e-8,
         "se": 14.741223951889612,
         "t": -6.425689384943973,
         "term": "age_c_sq"
       },
       {
         "ci_high": 190.6407036658827,
         "ci_low": -0.35003114817519076,
         "coef": 95.14533625885376,
         "p": 0.05082366181918291,
         "se": 47.91545665897092,
         "t": 1.985691943541986,
         "term": "age_c_post"
       },
       {
         "ci_high": 128.58105627438363,
         "ci_low": 68.02235865528931,
         "coef": 98.30170746483647,
         "p": 9.838490881307685e-9,
         "se": 15.192871287271776,
         "t": 6.470252107459851,
         "term": "age_c_post_sq"
       }
     ]
   }

   • dropped as collinear with the other regressors: ['age_c']

   Hashes SHA-256 e metadados de reprodução

   Digests dos dados

   carpenter_the_minimum_legal 445d2bbe7bb38003d078f082d967e07cd8383187389dffe2765cc6a38a5ce066

   sha256 do resultado

   3f41cb718497e4675c87101ee1c1028dbebae85bb08438ce095ad54abd929fbc

   Resultado mostrado ao modelo
4. 3Turno do modelo
   call_54b3525freferenceFim: 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.over = 163,0.",
       "caveats": [],
       "figure_refs": [
         "call_51b8b40b:coef.over"
       ]
     }

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

   Números encontrados

   163,0

   Sem lastro

   —

   Não citados

   —

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

   Estimativa (infer.ols): coef.over = 163,0.

   Figuras citadas: call_51b8b40b:coef.over · Parada: answered

   Figura da resposta: call_51b8b40b:coef.over = 162,989162.98920003605897 (se 30,333530.333516051790983)
7. 6Pontuação
   #0resposta com lastropassouobrigatóriaevery number backed
8. 7Pontuação
   #1figura da resposta próximafalhouobrigatória162.9892 vs 71.294

Épocas × verificações

Tentativas

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

• época 1, tentativa 1falhoupontuada(exibindo)

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