Tarefa causcibench_real.q027

Veredito

Pergunta feita ao analista

Does having access to electricity increase kerosene expenditures?

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
   thomas_effects_of— The dataset was collected to better understand the impacts of rural electrification at the household level, particularly in regions where electricity access was expanding but remained incomplete. The data comes from a household survey conducted across 686 households in 120 habitations in Uttar Pradesh, India. According to state regulations, households must be located within 40 meters of a power pole to be eligible for a legal electricity connection. Using this rule, the study sampled households situated 20-35 meters from the nearest pole, which were eligible to get electricity from the given pole, and 45-60 meters from the pole, those that were ineligible. Houses in the 35-45 meter range are excluded to minimize measurement error. The survey targeted areas with a balanced mix of electrified and non-electrified households and collected detailed information on household demographics, expenditures, appliance ownership and use, and daily activities. The variables are: food_expenditure: total monthly household expenditure on food in rupees education_expenditure: total monthly household expenditure on education in rupees kerosene_expenditure: total monthly household expenditure on kerosene in rupees total_expenditure: total monthly household expenditure in rupees age: age of the head of the household religion: 1 = Hindu, 0 = otherwise distance: distance of the household from the electric grid treat: 1 if the household is actually connected to the grid, 0 if not connected to the grid forcing: 1 if the household is eligible to get connected to the grid (within 40 meters), 0 if the household is not eligible to get connected to the grid kerosene_lamps: 1 = household has kerosene lamp, 0 = no kerosene lamp num_kerosene_lamps: number of kerosene wick lamps and lanterns owned by the household kerosene_lamp_hours: number of hours kerosene lamps are used daily kerosene_other: liters of kerosene used for other household purposes lighting_hours: total daily hours of household light usage child_lighting: daily hours of lighting used by children for reading and studying adult_lighting: daily hours of lighting used by adults for reading and studying child_activity: number of hours children spend at home in a given day adult_activity: number of hours adults spend at home in a given day appliances: number of appliances owned by the household appliance_use: number of daily hours using appliances by the household satisfaction_reliability: satisfaction with the reliability of lighting satisfaction_cost: satisfaction with the cost of lighting satisfaction_safety: satisfaction with the safety of lighting satisfaction_brightness: satisfaction with the brightness of lighting satisfaction: overall satisfaction with lighting satisfaction_chng: change in satisfaction with lighting over the past five years elec_value: willingness to pay for adequate electricity income_increase: belief that electrification will increase household income business_interest: interest in starting a new business due to electrification satisfaction_business: belief that electrification supports business aspirations aspirations: mean value of five questions measuring general aspirations knowledge: battery of questions related to knowledge of politics and popular culture
2. 2Chamada que gerou a resposta finalinfer.ols(Regressão linear por mínimos quadrados ordinários (MQO))citada na resposta

   Ver chamada call_87cf6347 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: iv
   Argumentos principais

   dataset:

   "thomas_effects_of"

   intercept:

   true

   level:

   0,950.95

   se:

   "HC1"

   x:

   ["treat"]

   y:

   "kerosene_expenditure"

   Resultado calculado

   • coef.treat = 11,8588 11.858841278821625
     Coefficient on treat in the regression of kerosene_expenditure
   Diagnósticos: n_obs = 686686 (observations used) · k = 22 (parameters estimated, fixed-effect dummies included) · r2 = 0,007661110.007661110244742586 · df_resid = 684684 · 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: 11,86
   Figuras citadas: call_87cf6347:coef.treat
   • ✓
     #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 having access to electricity increase kerosene expenditures?

   thomas_effects_of (agent)

   The dataset was collected to better understand the impacts of rural electrification at the household level, particularly in regions where electricity access was expanding but remained incomplete. The data comes from a household survey conducted across 686 households in 120 habitations in Uttar Pradesh, India. According to state regulations, households must be located within 40 meters of a power pole to be eligible for a legal electricity connection. Using this rule, the study sampled households situated 20-35 meters from the nearest pole, which were eligible to get electricity from the given pole, and 45-60 meters from the pole, those that were ineligible. Houses in the 35-45 meter range are excluded to minimize measurement error. The survey targeted areas with a balanced mix of electrified and non-electrified households and collected detailed information on household demographics, expenditures, appliance ownership and use, and daily activities. The variables are: food_expenditure: total monthly household expenditure on food in rupees education_expenditure: total monthly household expenditure on education in rupees kerosene_expenditure: total monthly household expenditure on kerosene in rupees total_expenditure: total monthly household expenditure in rupees age: age of the head of the household religion: 1 = Hindu, 0 = otherwise distance: distance of the household from the electric grid treat: 1 if the household is actually connected to the grid, 0 if not connected to the grid forcing: 1 if the household is eligible to get connected to the grid (within 40 meters), 0 if the household is not eligible to get connected to the grid kerosene_lamps: 1 = household has kerosene lamp, 0 = no kerosene lamp num_kerosene_lamps: number of kerosene wick lamps and lanterns owned by the household kerosene_lamp_hours: number of hours kerosene lamps are used daily kerosene_other: liters of kerosene used for other household purposes lighting_hours: total daily hours of household light usage child_lighting: daily hours of lighting used by children for reading and studying adult_lighting: daily hours of lighting used by adults for reading and studying child_activity: number of hours children spend at home in a given day adult_activity: number of hours adults spend at home in a given day appliances: number of appliances owned by the household appliance_use: number of daily hours using appliances by the household satisfaction_reliability: satisfaction with the reliability of lighting satisfaction_cost: satisfaction with the cost of lighting satisfaction_safety: satisfaction with the safety of lighting satisfaction_brightness: satisfaction with the brightness of lighting satisfaction: overall satisfaction with lighting satisfaction_chng: change in satisfaction with lighting over the past five years elec_value: willingness to pay for adequate electricity income_increase: belief that electrification will increase household income business_interest: interest in starting a new business due to electrification satisfaction_business: belief that electrification supports business aspirations aspirations: mean value of five questions measuring general aspirations knowledge: battery of questions related to knowledge of politics and popular culture

   file 4f4de9dbacecbfc6a5234e9fd8e9000d07aae7490af851fc4a6037813cb4e89a · frame —
2. 1Turno do modelo
   call_cbbf75f9naive-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": "thomas_effects_of",
       "x": [
         "treat"
       ],
       "y": "kerosene_expenditure"
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
3. 2Ferramenta
   infer.ols— Regressão linear por mínimos quadrados ordinários (MQO)v0.1.0call_87cf6347decidido pelo modeloreexecutável

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

   Argumentos

   {
     "cluster": null,
     "controls": [],
     "dataset": "thomas_effects_of",
     "fe": [],
     "intercept": true,
     "level": 0.95,
     "se": "HC1",
     "subset": null,
     "weights": null,
     "x": [
       "treat"
     ],
     "y": "kerosene_expenditure"
   }

   Figuras call_87cf6347

   Chave	Valor	Valor bruto	Erro-padrão	IC
   coef.treat Coefficient on treat in the regression of kerosene_expenditure	11,8588	11.858841278821625	6,080856.080848571266481	-0,0805294 – 23,7982

   Diagnósticos

   • n_obs = 686686 (observations used)
   • k = 22 (parameters estimated, fixed-effect dummies included)
   • r2 = 0,007661110.007661110244742586
   • df_resid = 684684
   • se_type = "HC1" (heteroskedasticity-robust standard errors (HC1))
   • n_clusters = — (errors not clustered)

   Derivação matemática e pressupostos

   coef.treat

   1. Coeficiente de treat na regressão por mínimos quadrados ordinários de kerosene_expenditure.→ 11,8588 (11.858841278821625)
   2. Erro-padrão robusto à heterocedasticidade (HC1): a raiz da variância estimada do coeficiente.ep = √V_jj→ 6,08085 (6.080848571266481)
   3. Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 684 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": 53.7739302532461,
         "ci_low": 44.45017122666933,
         "coef": 49.112050739957716,
         "p": 3.4072575586080197e-74,
         "se": 2.3743448483325014,
         "t": 20.684464084671202,
         "term": "Intercept"
       },
       {
         "ci_high": 23.79821200574238,
         "ci_low": -0.0805294480991261,
         "coef": 11.858841278821625,
         "p": 0.0515609965330903,
         "se": 6.080848571266481,
         "t": 1.9501951314587234,
         "term": "treat"
       }
     ]
   }

   • 1 rows were dropped because one of 'kerosene_expenditure', 'treat' was missing

   Hashes SHA-256 e metadados de reprodução

   Digests dos dados

   thomas_effects_of 6738107f98a584269eea39939b4755fca0f62827d7ae58d618a4490ed8f66eab

   sha256 do resultado

   5f7d965e47080fda83644dec7307f4f3d3b441421cd6c7fcfa860fe0ad09a19f

   Resultado mostrado ao modelo
4. 3Turno do modelo
   call_01cb7a91naive-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.treat = 11,86.",
       "caveats": [],
       "figure_refs": [
         "call_87cf6347:coef.treat"
       ]
     }

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

   Números encontrados

   11,86

   Sem lastro

   —

   Não citados

   —

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

   Estimativa ingênua (infer.ols): coef.treat = 11,86.

   Figuras citadas: call_87cf6347:coef.treat · Parada: answered

   Figura da resposta: call_87cf6347:coef.treat = 11,858811.858841278821625 (se 6,080856.080848571266481)
7. 6Pontuação
   #0resposta com lastropassouobrigatóriaevery number backed
8. 7Pontuação
   #1figura da resposta próximafalhouobrigatória11.85884128 vs 17.557

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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