Tarefa causcibench_real.q121

Veredito

Pergunta feita ao analista

Does enrollment in OSNAP lead to an increase in the number of whole grain calories consumed by a child?

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
   lee_impact— This dataset contains data describing dietary consumption of snacks served by afterschool programs in the OSNAP group randomized trial. This longitudinal dataset that covers baseline and follow periods of the trial was used to investigate the effectiveness of the OSNAP intervention on a number of children's dietary intake outcomes (e.g., changes in ounces of juice consumed, servings of fruits and vegetables, calories consumed). Dietary consumption data was collected on two days at baseline and two days at follow up for each child. Variables: siteid: Site indicator age: Age of the child in years male: Male or female identifier (0 = Female, 1 = Male) race: Race of the child (Black, Hispanic/Latino, Asian, Multiracial, Cape Verdean, Black Hispanic, -1 = missing) with White as reference childid: Unique child identifier foodservice: Satellite Foodservice provided (on-site, satellite, independent) osnap: Enrollment in OSNAP pair: Matched pairs obsdate: Observation date s_juiceoz_consumed: Ounces of 100% juice consumed per snack s_fv_consumed: Servings of fruits & vegetables consumed per snack s_pho_consumed: Servings of foods with trans fats consumed per snack s_wg_consumed: Servings of whole grains consumed per snack s_kcal_consumed: Calories(kcals) consumed per snack s_bevkcal_consumed: Beverage calories(kcals) consumed per snack sdayps: [description missing] post: Collected post-hoc firstday: day of data collection as a binary variable (0 not first day, 1 first day)
   lee_impact_ref
   Transformação "derive" aplicada à base "lee_impact_ref" (1377 → 1377 linhas). {"args":{"columns":["post","osnap"]},"name":"post_x_osnap","op":"interaction"}
2. 2Chamada que gerou a resposta finalinfer.ols(Regressão linear por mínimos quadrados ordinários (MQO))citada na respostacoincide com a referência

   Ver chamada call_99841da3 nos detalhes

   causcibench_metadata_rule/1: ols

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

   controls:

   ["post","osnap","age","male","race","firstday"]

   dataset:

   "lee_impact_ref"

   intercept:

   true

   level:

   0,950.95

   se:

   "HC1"

   x:

   ["post_x_osnap"]

   y:

   "s_wg_consumed"

   Resultado calculado

   • coef.post_x_osnap = 0,0957839 0.09578386162360664
     Coefficient on post_x_osnap in the regression of s_wg_consumed
   Diagnósticos: n_obs = 1.3771377 (observations used) · k = 88 (parameters estimated, fixed-effect dummies included) · r2 = 0,01317370.013173739402385087 · df_resid = 1.3691369 · 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: 0,09578
   Figuras citadas: call_99841da3:coef.post_x_osnap
   • ✓
     #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 enrollment in OSNAP lead to an increase in the number of whole grain calories consumed by a child?

   lee_impact (agent)

   This dataset contains data describing dietary consumption of snacks served by afterschool programs in the OSNAP group randomized trial. This longitudinal dataset that covers baseline and follow periods of the trial was used to investigate the effectiveness of the OSNAP intervention on a number of children's dietary intake outcomes (e.g., changes in ounces of juice consumed, servings of fruits and vegetables, calories consumed). Dietary consumption data was collected on two days at baseline and two days at follow up for each child. Variables: siteid: Site indicator age: Age of the child in years male: Male or female identifier (0 = Female, 1 = Male) race: Race of the child (Black, Hispanic/Latino, Asian, Multiracial, Cape Verdean, Black Hispanic, -1 = missing) with White as reference childid: Unique child identifier foodservice: Satellite Foodservice provided (on-site, satellite, independent) osnap: Enrollment in OSNAP pair: Matched pairs obsdate: Observation date s_juiceoz_consumed: Ounces of 100% juice consumed per snack s_fv_consumed: Servings of fruits & vegetables consumed per snack s_pho_consumed: Servings of foods with trans fats consumed per snack s_wg_consumed: Servings of whole grains consumed per snack s_kcal_consumed: Calories(kcals) consumed per snack s_bevkcal_consumed: Beverage calories(kcals) consumed per snack sdayps: [description missing] post: Collected post-hoc firstday: day of data collection as a binary variable (0 not first day, 1 first day)

   file 1745419e067c9258af66c2ec5de4fd0428b8db184554eda40436ed9c96dc3eac · frame —

   lee_impact_ref (reference)

   file 1745419e067c9258af66c2ec5de4fd0428b8db184554eda40436ed9c96dc3eac · frame —

   1. derive {"args":{"columns":["post","osnap"]},"name":"post_x_osnap","op":"interaction"}
2. 1Transformação
   lee_impact_ref · derive {"args":{"columns":["post","osnap"]},"name":"post_x_osnap","op":"interaction"}

   1377 → 1377 linhas

   Frame antes → depois: 553144678947 → d2d3c02060df
3. 2Turno do modelo
   call_910e028areferenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

   Texto visível

   causcibench_metadata_rule/1: ols

   Chamadas pedidas

   • infer.ols

     {
       "controls": [
         "post",
         "osnap",
         "age",
         "male",
         "race",
         "firstday"
       ],
       "dataset": "lee_impact_ref",
       "x": [
         "post_x_osnap"
       ],
       "y": "s_wg_consumed"
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
4. 3Ferramenta
   infer.ols— Regressão linear por mínimos quadrados ordinários (MQO)v0.1.0call_99841da3decidido pelo modeloreexecutável

   Motivo: causcibench_metadata_rule/1: ols

   Argumentos

   {
     "cluster": null,
     "controls": [
       "post",
       "osnap",
       "age",
       "male",
       "race",
       "firstday"
     ],
     "dataset": "lee_impact_ref",
     "fe": [],
     "intercept": true,
     "level": 0.95,
     "se": "HC1",
     "subset": null,
     "weights": null,
     "x": [
       "post_x_osnap"
     ],
     "y": "s_wg_consumed"
   }

   Figuras call_99841da3

   Chave	Valor	Valor bruto	Erro-padrão	IC
   coef.post_x_osnap Coefficient on post_x_osnap in the regression of s_wg_consumed	0,0957839	0.09578386162360664	0,04281990.042819942360303884	0,0117841 – 0,179784

   Diagnósticos

   • n_obs = 1.3771377 (observations used)
   • k = 88 (parameters estimated, fixed-effect dummies included)
   • r2 = 0,01317370.013173739402385087
   • df_resid = 1.3691369
   • se_type = "HC1" (heteroskedasticity-robust standard errors (HC1))
   • n_clusters = — (errors not clustered)

   Derivação matemática e pressupostos

   coef.post_x_osnap

   1. Coeficiente de post_x_osnap na regressão por mínimos quadrados ordinários de s_wg_consumed.→ 0,0957839 (0.09578386162360664)
   2. Erro-padrão robusto à heterocedasticidade (HC1): a raiz da variância estimada do coeficiente.ep = √V_jj→ 0,0428199 (0.042819942360303884)
   3. Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 1369 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": 0.27181527710766856,
         "ci_low": 0.07248382412662568,
         "coef": 0.17214955061714712,
         "p": 0.0007230346978146343,
         "se": 0.05080583716439131,
         "t": 3.388381340122921,
         "term": "Intercept"
       },
       {
         "ci_high": 0.17978367152366007,
         "ci_low": 0.011784051723553207,
         "coef": 0.09578386162360664,
         "p": 0.025453444080176163,
         "se": 0.042819942360303884,
         "t": 2.2368984249825337,
         "term": "post_x_osnap"
       },
       {
         "ci_high": 0.00326570736138547,
         "ci_low": -0.11611023516998137,
         "coef": -0.05642226390429795,
         "p": 0.06390145836032704,
         "se": 0.03042668182512652,
         "t": -1.8543679599562557,
         "term": "post"
       },
       {
         "ci_high": 0.01591517796519576,
         "ci_low": -0.09715471268571738,
         "coef": -0.04061976736026081,
         "p": 0.15892638133924383,
         "se": 0.02881938784217941,
         "t": -1.4094597561441131,
         "term": "osnap"
       },
       {
         "ci_high": 0.012219295260514696,
         "ci_low": -0.011302574743961036,
         "coef": 0.0004583602582768298,
         "p": 0.9390694841768665,
         "se": 0.0059952821262133065,
         "t": 0.07645349270099083,
         "term": "age"
       },
       {
         "ci_high": 0.03882728656311895,
         "ci_low": -0.0442401085055549,
         "coef": -0.002706410971217976,
         "p": 0.8983040092034156,
         "se": 0.021172316181985448,
         "t": -0.1278278176064996,
         "term": "male"
       },
       {
         "ci_high": 0.0004972054260928057,
         "ci_low": -0.024683831529399083,
         "coef": -0.01209331305165314,
         "p": 0.05974560672451491,
         "se": 0.0064181725666391,
         "t": -1.8842299620475689,
         "term": "race"
       },
       {
         "ci_high": 0.10480637729577862,
         "ci_low": 0.020843274642863605,
         "coef": 0.06282482596932111,
         "p": 0.003383997862323851,
         "se": 0.02140061519346255,
         "t": 2.9356551389472583,
         "term": "firstday"
       }
     ]
   }

   Hashes SHA-256 e metadados de reprodução

   Digests dos dados

   lee_impact_ref d2d3c02060dfdfb62ae16ba8672caff7e6638c3cf10a320b65d5e71455768cf1

   sha256 do resultado

   fa89140645774cbdc35ed9a1785906a7065dc8ee738ba90bbad4120e6167513a

   Resultado mostrado ao modelo
5. 4Turno do modelo
   call_29601f17referenceFim: 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_x_osnap = 0,09578.",
       "caveats": [],
       "figure_refs": [
         "call_99841da3:coef.post_x_osnap"
       ]
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
6. 5Guarda
   resposta aceita

   Números encontrados

   0,09578

   Sem lastro

   —

   Não citados

   —

   1 nova(s) tentativa(s) restante(s)
7. 6Resposta

   Estimativa (infer.ols): coef.post_x_osnap = 0,09578.

   Figuras citadas: call_99841da3:coef.post_x_osnap · Parada: answered

   Figura da resposta: call_99841da3:coef.post_x_osnap = 0,09578390.09578386162360664 (se 0,04281990.042819942360303884)
8. 7Pontuação
   #0resposta com lastropassouobrigatóriaevery number backed
9. 8Pontuação
   #1figura da resposta próximapassouobrigatória0.09578386162 vs 0.095

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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