Tarefa causcibench_real.q031

Veredito

Pergunta feita ao analista

Does receiving a DUI conviction (BAC ≥ 0.08) reduce the likelihood of future drunk driving compared to those just below the legal limit?

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
   hansen_punishment— This dataset contains administrative records from the state of Washington related to driving under the influence (DUI) incidents. Washington State uniformly enforced a legal BAC limit of 0.08 for DUI offenses. The BAC values are obtained from tests conducted by the state police using breathalyzers. Drivers under the legal drinking age were excluded from the analysis, as they are subject to different legal standards. Besides BAC values, the dataset includes demographic information about the driver and recidivism, which indicates if the person was charged with a DUI offense again. Variables: date: date when the BAC test occurred alcohol1: alcohol level from the first test alcohol2: alcohol level from the second test low_score: lower score between alcohol1 and alcohol2 male: indicator for the driver's gender; 1 if male, 0 otherwise white: indicator for the driver's race; 1 if white, 0 otherwise recidivism: indicator for whether the driver was stopped again for suspected DUI within a specified follow-up period; 1 if yes, 0 if no acc: indicator for whether the original DUI stop involved a vehicle accident; 1 if yes, 0 if no age: age of the driver at the time of the DUI stop year: calendar year in which the DUI stop occurred bac1: BAC value at the time of the test
2. 2Chamada que gerou a resposta finalinfer.rdd(Regressão descontínua (RDD))citada na respostacoincide com a referência

   Ver chamada call_82d917c3 nos detalhes

   causcibench_metadata_rule/1: rdd

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

   cutoff:

   0,080.08

   dataset:

   "hansen_punishment"

   kernel:

   "triangular"

   level:

   0,950.95

   polynomial:

   11

   running:

   "bac1"

   y:

   "recidivism"

   Resultado calculado

   • effect = -0,0165016 -0.016501635558275784
     Jump in recidivism at the cutoff of bac1
   Diagnósticos: bandwidth = 0,03554010.035540149106476245 (half-width of the window around the cutoff (Imbens–Kalyanaraman)) · bandwidth_method = "IK" · n_left = 16.33916339 (observations with positive weight below the cutoff) · n_right = 48.18248182 (observations with positive weight at or above the cutoff) · kernel = "triangular" · polynomial = 11 · bandwidth_sensitive = false (the estimates at half and double the bandwidth fall inside the main interval) · n_obs = 214.558214558 (rows used to choose the bandwidth) · 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: -0,01650
   Figuras citadas: call_82d917c3:effect
   • ✓
     #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 receiving a DUI conviction (BAC ≥ 0.08) reduce the likelihood of future drunk driving compared to those just below the legal limit?

   hansen_punishment (agent)

   This dataset contains administrative records from the state of Washington related to driving under the influence (DUI) incidents. Washington State uniformly enforced a legal BAC limit of 0.08 for DUI offenses. The BAC values are obtained from tests conducted by the state police using breathalyzers. Drivers under the legal drinking age were excluded from the analysis, as they are subject to different legal standards. Besides BAC values, the dataset includes demographic information about the driver and recidivism, which indicates if the person was charged with a DUI offense again. Variables: date: date when the BAC test occurred alcohol1: alcohol level from the first test alcohol2: alcohol level from the second test low_score: lower score between alcohol1 and alcohol2 male: indicator for the driver's gender; 1 if male, 0 otherwise white: indicator for the driver's race; 1 if white, 0 otherwise recidivism: indicator for whether the driver was stopped again for suspected DUI within a specified follow-up period; 1 if yes, 0 if no acc: indicator for whether the original DUI stop involved a vehicle accident; 1 if yes, 0 if no age: age of the driver at the time of the DUI stop year: calendar year in which the DUI stop occurred bac1: BAC value at the time of the test

   file 16b9aa8145387ae7818091846e56c7754b095fbcfc2e008a045a47bad7054e2c · frame —
2. 1Turno do modelo
   call_f3c1138areferenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

   Texto visível

   causcibench_metadata_rule/1: rdd

   Chamadas pedidas

   • infer.rdd

     {
       "cutoff": 0.08,
       "dataset": "hansen_punishment",
       "running": "bac1",
       "y": "recidivism"
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
3. 2Ferramenta
   infer.rdd— Regressão descontínua (RDD)v0.1.0call_82d917c3decidido pelo modeloreexecutável

   Motivo: causcibench_metadata_rule/1: rdd

   Argumentos

   {
     "bandwidth": null,
     "cluster": null,
     "cutoff": 0.08,
     "dataset": "hansen_punishment",
     "kernel": "triangular",
     "level": 0.95,
     "polynomial": 1,
     "running": "bac1",
     "subset": null,
     "y": "recidivism"
   }

   Figuras call_82d917c3

   Chave	Valor	Valor bruto	Erro-padrão	IC
   effect Jump in recidivism at the cutoff of bac1	-0,0165016	-0.016501635558275784	0,005628840.005628837615927555	-0,027534 – -0,00546932

   Diagnósticos

   • bandwidth = 0,03554010.035540149106476245 (half-width of the window around the cutoff (Imbens–Kalyanaraman))
   • bandwidth_method = "IK"
   • n_left = 16.33916339 (observations with positive weight below the cutoff)
   • n_right = 48.18248182 (observations with positive weight at or above the cutoff)
   • kernel = "triangular"
   • polynomial = 11
   • bandwidth_sensitive = false (the estimates at half and double the bandwidth fall inside the main interval)
   • n_obs = 214.558214558 (rows used to choose the bandwidth)
   • se_type = "HC1" (heteroskedasticity-robust standard errors (HC1))

   Derivação matemática e pressupostos

   effect

   1. Ajuste linear ponderado (kernel triangular) de cada lado do limiar, com janela h (Imbens–Kalyanaraman); o efeito é a diferença entre os dois ajustes no limiar.→ -0,0165016 (-0.016501635558275784)
   2. Erro-padrão robusto à heterocedasticidade (HC1): a raiz da variância estimada do coeficiente.ep = √V_jj→ 0,00562884 (0.005628837615927555)
   3. Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição normal vezes o erro-padrão.IC = τ ± c · ep

   Pressupostos do estimador

   • units cannot precisely control the running variable around the cutoff
   • every other determinant of the outcome is continuous at the cutoff

   Tabelas (1)

   {
     "sensitivity": [
       {
         "bandwidth": 0.017770074553238122,
         "ci_high": 0.0010246275194371844,
         "ci_low": -0.029200470568340946,
         "estimate": -0.014087921524451881,
         "multiplier": 0.5,
         "n_left": 11684,
         "n_right": 21237,
         "se": 0.007710625890626014
       },
       {
         "bandwidth": 0.035540149106476245,
         "ci_high": -0.005469316556233474,
         "ci_low": -0.027533954560318093,
         "estimate": -0.016501635558275784,
         "multiplier": 1,
         "n_left": 16339,
         "n_right": 48182,
         "se": 0.005628837615927555
       },
       {
         "bandwidth": 0.07108029821295249,
         "ci_high": -0.012365363683707728,
         "ci_low": -0.029187490192206282,
         "estimate": -0.020776426937957004,
         "multiplier": 2,
         "n_left": 19543,
         "n_right": 105541,
         "se": 0.004291437659362453
       }
     ]
   }

   Hashes SHA-256 e metadados de reprodução

   Digests dos dados

   hansen_punishment 0f2d1ddc701815af6b01033059ca026f45e63491721e9eafd8327e23cfaa05f8

   sha256 do resultado

   9be4347c259842011138501a2d98caa229ef205a5f159cc45365136a8c8687a8

   Resultado mostrado ao modelo
4. 3Turno do modelo
   call_bc1bc47creferenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

   Chamadas pedidas

   • submit_answer (meta-ferramenta)

     {
       "answer": "Estimativa (infer.rdd): effect = -0,01650.",
       "caveats": [],
       "figure_refs": [
         "call_82d917c3:effect"
       ]
     }

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

   Números encontrados

   -0,01650

   Sem lastro

   —

   Não citados

   —

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

   Estimativa (infer.rdd): effect = -0,01650.

   Figuras citadas: call_82d917c3:effect · Parada: answered

   Figura da resposta: call_82d917c3:effect = -0,0165016-0.016501635558275784 (se 0,005628840.005628837615927555)
7. 6Pontuação
   #0resposta com lastropassouobrigatóriaevery number backed
8. 7Pontuação
   #1figura da resposta próximafalhouobrigatória-0.01650163556 vs -0.026

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)

Downloads desta tarefa