Tarefa causcibench_real.q088

Veredito

Pergunta feita ao analista

Did the Head Start program help reduce the child mortality 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
   calonico_regression— In 1965, the U.S. government launched the Head Start program to provide financial assistance to poor families. One of its primary goals was to reduce child mortality by expanding access to essential health services. To determine eligibility for Head Start funds, county-level poverty rates were calculated using data from the 1960 census. Counties with a poverty rate greater than 59.1984 were deemed eligible to apply for grants. Data variables: povrate60: Poverty rate constructed using data from the 1960 census mort_age59_related_postHS: Mortality rate based on data after the rollout of the Head Start program census1960_pop: Population based on the 1960 census census1960_pctsch1417: Percentage of 14-17 year olds enrolled in school census1960_pctsch534: Percentage of 5-34 year olds with formal education in schools census1960_pctsch25plus: Percentage of people older than 25 with formal education in schools census1960_pop1417: Number of individuals aged 14-17 census1960_pop534: Number of individuals aged 5-34 census1960_pop25plus: Number of individuals older than 25 census1960_pcturban: Percentage of individuals living in urban areas of the county census1960_pctblack: Percentage of individuals who are Black
2. 2Chamada que gerou a resposta finalinfer.rdd(Regressão descontínua (RDD))citada na respostacoincide com a referência

   Ver chamada call_4b2e9e3f nos detalhes

   causcibench_metadata_rule/1: rdd

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

   cutoff:

   59,198459.1984

   dataset:

   "calonico_regression"

   kernel:

   "triangular"

   level:

   0,950.95

   polynomial:

   11

   running:

   "povrate60"

   y:

   "mort_age59_related_postHS"

   Resultado calculado

   • effect = -2,39107 -2.391068713991865
     Jump in mort_age59_related_postHS at the cutoff of povrate60
   Diagnósticos: bandwidth = 6,90316.903096140946374 (half-width of the window around the cutoff (Imbens–Kalyanaraman)) · bandwidth_method = "IK" · n_left = 237237 (observations with positive weight below the cutoff) · n_right = 183183 (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 = 2.7832783 (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: -2,391
   Figuras citadas: call_4b2e9e3f: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

   Did the Head Start program help reduce the child mortality rates?

   calonico_regression (agent)

   In 1965, the U.S. government launched the Head Start program to provide financial assistance to poor families. One of its primary goals was to reduce child mortality by expanding access to essential health services. To determine eligibility for Head Start funds, county-level poverty rates were calculated using data from the 1960 census. Counties with a poverty rate greater than 59.1984 were deemed eligible to apply for grants. Data variables: povrate60: Poverty rate constructed using data from the 1960 census mort_age59_related_postHS: Mortality rate based on data after the rollout of the Head Start program census1960_pop: Population based on the 1960 census census1960_pctsch1417: Percentage of 14-17 year olds enrolled in school census1960_pctsch534: Percentage of 5-34 year olds with formal education in schools census1960_pctsch25plus: Percentage of people older than 25 with formal education in schools census1960_pop1417: Number of individuals aged 14-17 census1960_pop534: Number of individuals aged 5-34 census1960_pop25plus: Number of individuals older than 25 census1960_pcturban: Percentage of individuals living in urban areas of the county census1960_pctblack: Percentage of individuals who are Black

   file 32fd83152c3fa34ed8331f184098ff9e8b85dc7e99f59a0ecf027d4573e0d86f · frame —
2. 1Turno do modelo
   call_739aec2freferenceFim: 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": 59.1984,
       "dataset": "calonico_regression",
       "running": "povrate60",
       "y": "mort_age59_related_postHS"
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
3. 2Ferramenta
   infer.rdd— Regressão descontínua (RDD)v0.1.0call_4b2e9e3fdecidido pelo modeloreexecutável

   Motivo: causcibench_metadata_rule/1: rdd

   Argumentos

   {
     "bandwidth": null,
     "cluster": null,
     "cutoff": 59.1984,
     "dataset": "calonico_regression",
     "kernel": "triangular",
     "level": 0.95,
     "polynomial": 1,
     "running": "povrate60",
     "subset": null,
     "y": "mort_age59_related_postHS"
   }

   Figuras call_4b2e9e3f

   Chave	Valor	Valor bruto	Erro-padrão	IC
   effect Jump in mort_age59_related_postHS at the cutoff of povrate60	-2,39107	-2.391068713991865	1,132981.1329834192727755	-4,61168 – -0,170462

   Diagnósticos

   • bandwidth = 6,90316.903096140946374 (half-width of the window around the cutoff (Imbens–Kalyanaraman))
   • bandwidth_method = "IK"
   • n_left = 237237 (observations with positive weight below the cutoff)
   • n_right = 183183 (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 = 2.7832783 (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.→ -2,39107 (-2.391068713991865)
   2. Erro-padrão robusto à heterocedasticidade (HC1): a raiz da variância estimada do coeficiente.ep = √V_jj→ 1,13298 (1.1329834192727755)
   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": 3.451548070473187,
         "ci_high": -1.1381462183521744,
         "ci_low": -5.7179364647050255,
         "estimate": -3.4280413415286,
         "multiplier": 0.5,
         "n_left": 107,
         "n_right": 99,
         "se": 1.1683353067907503
       },
       {
         "bandwidth": 6.903096140946374,
         "ci_high": -0.17046201713618148,
         "ci_low": -4.611675410847548,
         "estimate": -2.391068713991865,
         "multiplier": 1,
         "n_left": 237,
         "n_right": 183,
         "se": 1.1329834192727755
       },
       {
         "bandwidth": 13.806192281892748,
         "ci_high": -0.22278164512566812,
         "ci_low": -3.603542846115129,
         "estimate": -1.9131622456203987,
         "multiplier": 2,
         "n_left": 495,
         "n_right": 256,
         "se": 0.8624549296967889
       }
     ]
   }

   • 26 rows were dropped because one of 'mort_age59_related_postHS', 'povrate60' was missing

   Hashes SHA-256 e metadados de reprodução

   Digests dos dados

   calonico_regression 14c33c8983a9e1ace4167652ca00697e218ebe3017a29344874847585e3ee156

   sha256 do resultado

   4677a1a724d0ad1e18a9bf57091ca390fedcf029ab3dcf8ffcc6595964a11291

   Resultado mostrado ao modelo
4. 3Turno do modelo
   call_6ac29364referenceFim: 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 = -2,391.",
       "caveats": [],
       "figure_refs": [
         "call_4b2e9e3f:effect"
       ]
     }

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

   Números encontrados

   -2,391

   Sem lastro

   —

   Não citados

   —

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

   Estimativa (infer.rdd): effect = -2,391.

   Figuras citadas: call_4b2e9e3f:effect · Parada: answered

   Figura da resposta: call_4b2e9e3f:effect = -2,39107-2.391068713991865 (se 1,132981.1329834192727755)
7. 6Pontuação
   #0resposta com lastropassouobrigatóriaevery number backed
8. 7Pontuação
   #1figura da resposta próximapassouobrigatória-2.391068714 vs -2.409

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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