Tarefa causcibench_real.q057

Veredito

Pergunta feita ao analista

Does number of sibilings have an effect on the years of education?

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
   xiong_effect_of— The dataset originates from the Chinese General Social Survey (CGSS), which was designed to systematically investigate social and demographic influences on education in China. It contains information at the individual, household, and regional levels, enabling analysis of how family structure and parental background shape children's educational outcomes. Covering the period after China's reform and opening-up, the survey integrates both family-level characteristics (e.g., number of siblings, birth order, parental education and occupation) and broader social context (e.g., urban-rural differences, regional disparities). Overall, the data provides a foundation for studying how internal family dynamics interact with external socio-economic conditions to influence educational attainment. Data variables: year_edu: Educational years sib: Number of siblings period: After the reform and opening up han: Han nationality gender: Gender urban_father: Household type of father job_father: Father works in a public institution job_mother: Mother works in a public institution party_father: Father is a party member party_mother: Mother is a party member edu_father: Education level of father edu_mother: Education level of mother non_father: Fatherless child (age 14) non_mother: Motherless child (age 14) level_edu: Degree level of education total_income: Total family income province: Province countyid: County ID sequence: Child's birth order only_child: Indicator if the child is an only child eldest_child: Indicator if the child is the eldest youngest_child: Indicator if the child is the youngest
2. 2Chamada que gerou a resposta finalinfer.ols(Regressão linear por mínimos quadrados ordinários (MQO))citada na resposta

   Ver chamada call_2d748329 nos detalhes

   Linha de base ingênua: regressão do resultado no tratamento, sem controles.

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

   dataset:

   "xiong_effect_of"

   intercept:

   true

   level:

   0,950.95

   se:

   "HC1"

   x:

   ["sib"]

   y:

   "year_edu"

   Resultado calculado

   • coef.sib = -0,627042 -0.6270423965245858
     Coefficient on sib in the regression of year_edu
   Diagnósticos: n_obs = 5.4915491 (observations used) · k = 22 (parameters estimated, fixed-effect dummies included) · r2 = 0,1084870.10848726139435316 · df_resid = 5.4895489 · 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: -0,6270
   Figuras citadas: call_2d748329:coef.sib
   • ✓
     #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 number of sibilings have an effect on the years of education?

   xiong_effect_of (agent)

   The dataset originates from the Chinese General Social Survey (CGSS), which was designed to systematically investigate social and demographic influences on education in China. It contains information at the individual, household, and regional levels, enabling analysis of how family structure and parental background shape children's educational outcomes. Covering the period after China's reform and opening-up, the survey integrates both family-level characteristics (e.g., number of siblings, birth order, parental education and occupation) and broader social context (e.g., urban-rural differences, regional disparities). Overall, the data provides a foundation for studying how internal family dynamics interact with external socio-economic conditions to influence educational attainment. Data variables: year_edu: Educational years sib: Number of siblings period: After the reform and opening up han: Han nationality gender: Gender urban_father: Household type of father job_father: Father works in a public institution job_mother: Mother works in a public institution party_father: Father is a party member party_mother: Mother is a party member edu_father: Education level of father edu_mother: Education level of mother non_father: Fatherless child (age 14) non_mother: Motherless child (age 14) level_edu: Degree level of education total_income: Total family income province: Province countyid: County ID sequence: Child's birth order only_child: Indicator if the child is an only child eldest_child: Indicator if the child is the eldest youngest_child: Indicator if the child is the youngest

   file 9f3ae5d6ca9461cc595be7c9b567c126215c2388ed7e73a9777f3016a813261f · frame —
2. 1Turno do modelo
   call_fcf174e5naive-olsFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

   Texto visível

   Linha de base ingênua: regressão do resultado no tratamento, sem controles.

   Chamadas pedidas

   • infer.ols

     {
       "dataset": "xiong_effect_of",
       "x": [
         "sib"
       ],
       "y": "year_edu"
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
3. 2Ferramenta
   infer.ols— Regressão linear por mínimos quadrados ordinários (MQO)v0.1.0call_2d748329decidido pelo modeloreexecutável

   Motivo: Linha de base ingênua: regressão do resultado no tratamento, sem controles.

   Argumentos

   {
     "cluster": null,
     "controls": [],
     "dataset": "xiong_effect_of",
     "fe": [],
     "intercept": true,
     "level": 0.95,
     "se": "HC1",
     "subset": null,
     "weights": null,
     "x": [
       "sib"
     ],
     "y": "year_edu"
   }

   Figuras call_2d748329

   Chave	Valor	Valor bruto	Erro-padrão	IC
   coef.sib Coefficient on sib in the regression of year_edu	-0,627042	-0.6270423965245858	0,02432640.024326364998656043	-0,674732 – -0,579353

   Diagnósticos

   • n_obs = 5.4915491 (observations used)
   • k = 22 (parameters estimated, fixed-effect dummies included)
   • r2 = 0,1084870.10848726139435316
   • df_resid = 5.4895489
   • se_type = "HC1" (heteroskedasticity-robust standard errors (HC1))
   • n_clusters = — (errors not clustered)

   Derivação matemática e pressupostos

   coef.sib

   1. Coeficiente de sib na regressão por mínimos quadrados ordinários de year_edu.→ -0,627042 (-0.6270423965245858)
   2. Erro-padrão robusto à heterocedasticidade (HC1): a raiz da variância estimada do coeficiente.ep = √V_jj→ 0,0243264 (0.024326364998656043)
   3. Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 5489 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": 11.331887267198752,
         "ci_low": 10.994850708069105,
         "coef": 11.163368987633929,
         "p": 0,
         "se": 0.08596133475845212,
         "t": 129.86500289929822,
         "term": "Intercept"
       },
       {
         "ci_high": -0.5793530814487008,
         "ci_low": -0.6747317116004709,
         "coef": -0.6270423965245858,
         "p": 2.1116504256092388e-138,
         "se": 0.024326364998656043,
         "t": -25.776247152389104,
         "term": "sib"
       }
     ]
   }

   • 509 rows were dropped because one of 'year_edu', 'sib' was missing

   Hashes SHA-256 e metadados de reprodução

   Digests dos dados

   xiong_effect_of 1b514f0af8f160f17ac8aa8cfb2c99c88878acfb7efa96cd21b49085f6bbbb4f

   sha256 do resultado

   b4f405d1e7815244a07311aadeda194f5b37b2883fcc772cbc0716d9a8efdc08

   Resultado mostrado ao modelo
4. 3Turno do modelo
   call_15d8b959naive-olsFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

   Chamadas pedidas

   • submit_answer (meta-ferramenta)

     {
       "answer": "Estimativa ingênua (infer.ols): coef.sib = -0,6270.",
       "caveats": [],
       "figure_refs": [
         "call_2d748329:coef.sib"
       ]
     }

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

   Números encontrados

   -0,6270

   Sem lastro

   —

   Não citados

   —

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

   Estimativa ingênua (infer.ols): coef.sib = -0,6270.

   Figuras citadas: call_2d748329:coef.sib · Parada: answered

   Figura da resposta: call_2d748329:coef.sib = -0,627042-0.6270423965245858 (se 0,02432640.024326364998656043)
7. 6Pontuação
   #0resposta com lastropassouobrigatóriaevery number backed
8. 7Pontuação
   #1figura da resposta próximafalhouobrigatória-0.6270423965 vs -0.308

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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