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Tarefa causcibench_real.q027

causcibench_realagentT2publicépoca 1, tentativa 1Execução: 20261015T171142Z-suite-causcibench_real-naive-d6494d

Veredito

falhouDecidida pela verificação 1: figura da resposta próxima, fora da tolerância.
11.85884128 vs 17.557

Família do método (das chamadas de ferramentas): ols (regression)

Pergunta feita ao analista

Does having access to electricity increase kerosene expenditures?

Resposta

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

Valores esperados e de onde vêm

causcibench_real.gold.q027

Fonte
CauSciBench real split, data/metadata_json/real_input.json at d9bb43a, id 27
Tipo
benchmark_gold
Localização
real_input.json, record id 27: fields effect and std_error
Trecho
id 27 effect 17.557 std_error 8.611
Valor como publicado
17.557
Erro-padrão como publicado
8.611
Valor
17,55717.557 (se 8,6118.611)
Conferido por
arquivo do benchmark · Written by scripts/freeze_causcibench_real.py from the pinned file; equal to the `answer` and `std_error` of the same id in data/metadata_csv/real_input.csv at the pin.
sha256 da cópia lida
c1f4c8930675afff63d816d8ed066941ae92697e7bfc31e9d38519398dd80438 (identifica a cópia lida, não um arquivo canônico)
Endereço
https://raw.githubusercontent.com/causalNLP/CauSciBench/d9bb43a2d90f261978cc5ac0b628c0484f6e8956/data/metadata_json/real_input.json
Licença
CauSciBench repository (MIT for code; metadata as published in the repository)
  • #1 figura da resposta próxima: 17,55717.557 · relativa 0.05

Verificações

Cada verificação, o que esperava, o que observou e a chamada que leu
#VerificaçãoObrigatóriaResultadoEsperadoObservadoTolerânciaFonteChamada
0resposta com lastro
every number backed
obrigatóriapassou—[]———
1figura da resposta próxima
11.85884128 vs 17.557
obrigatóriafalhou17,55717.55711,858811.858841278821625relativa 0.05causcibench_real.gold.q027call_87cf6347

Caminho de referência e caminho do agente

A chamada de referência ao lado da chamada que deu a resposta, argumento por argumento.

caminho de referência do item · metadata_rule

Referência: infer.iv_2slsChamada do agente: infer.ols (call_87cf6347)diferente
Argumentos da referência e do agente
ArgumentoReferênciaAgente
cluster——não fixado pela referência
controls—[]não fixado pela referência
dataset"thomas_effects_of""thomas_effects_of"igual
endog["treat"]—ausente
exog["gender","birthplace","age","religion","caste","Pole"]—ausente
fe—[]não fixado pela referência
instruments["forcing"]—ausente
intercept—truenão fixado pela referência
level—0,950.95não fixado pela referência
se—"HC1"não fixado pela referência
subset——não fixado pela referência
weights——não fixado pela referência
x—["treat"]não fixado pela referência
y"kerosene_expenditure""kerosene_expenditure"igual

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"
      }
  3. 2Ferramenta
    infer.olsv0.1.0call_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
    ChaveValorValor brutoErro-padrãoIC
    coef.treat
    Coefficient on treat in the regression of kerosene_expenditure
    11,8588 11.8588412788216256,080856.080848571266481-0,0805294 – 23,7982
    Diagnósticos
    • n_obs = 686686
    • k = 22
    • r2 = 0,007661110.007661110244742586
    • df_resid = 684684
    • se_type = "HC1"
    • n_clusters = —
    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
    Digests dos dados
    thomas_effects_of 6738107f98a584269eea39939b4755fca0f62827d7ae58d618a4490ed8f66eab
    sha256 do resultado
    5f7d965e47080fda83644dec7307f4f3d3b441421cd6c7fcfa860fe0ad09a19f
  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"
        ]
      }
  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

Resultado de cada verificação em cada época pontuada
#01
Época 1passoufalhou

Tentativas

Cada tentativa é seu próprio arquivo e é publicada; só a última de cada época é pontuada.

Downloads desta tarefa

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