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Tarefa causcibench_real.q125

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

Veredito

falhouDecidida pela verificação 1: figura da resposta próxima, fora da tolerância.
1.215283165 vs 0.904

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

Pergunta feita ao analista

What is the causal effect of having a sow insured on the subsequent number of sows owned by a farmer?

Resposta

Estimativa ingênua (infer.ols): coef.insuredpig = 1,215.

Valores esperados e de onde vêm

causcibench_real.gold.q125

Fonte
CauSciBench real split, data/metadata_json/real_input.json at d9bb43a, id 125
Tipo
benchmark_gold
Localização
real_input.json, record id 125: fields effect and std_error
Trecho
id 125 effect 0.904 std_error 0.24
Valor como publicado
0.904
Erro-padrão como publicado
0.24
Valor
0,9040.904 (se 0,240.24)
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: 0,9040.904 · 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
1.215283165 vs 0.904
obrigatóriafalhou0,9040.9041,215281.2152831647206284relativa 0.05causcibench_real.gold.q125call_68c19b7f

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_68c19b7f)diferente
Argumentos da referência e do agente
ArgumentoReferênciaAgente
cluster——não fixado pela referência
controls—[]não fixado pela referência
dataset"cai_effect_microinsurance""cai_effect_microinsurance"igual
endog["insuredpig"]—ausente
exog[]—ausente
fe—[]não fixado pela referência
instruments["groupd2","groupd3"]—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—["insuredpig"]não fixado pela referência
y"sow3""sow3"igual

Trajetória

Cada evento na ordem em que aconteceu: turnos do modelo, chamadas de ferramentas, guardas, transformações e pontuação.

  1. 0Início

    What is the causal effect of having a sow insured on the subsequent number of sows owned by a farmer?

    cai_effect_microinsurance (agent)

    The dataset is the result of a randomized controlled trial conducted to study the effect of insurance on farming behavior. Each village consists of at least one AHW (Animal Husbandry Worker), who is involved in tasks such as animal immunization, providing technical assistance to farmers, etc. As part of the experiment, AHWs are assigned to different incentive schemes associated with the number of sow insurance purchases. In the control group villages, AHWs were offered a reward of 50 yuan to participate. In the low incentive group (LIG), the AHW is offered 20 Yuan to begin with, and an additional payment of 2 Yuan for each insured sow. Finally, in the high incentive group (HIG), the AHWs are offered a starting payment of 20 Yuan and an additional 4 Yuan for each insured sow. At the end of the experiment, data on total sows in each village were obtained. Variables: sow0: No. of Sows in Dec. 2006 sow1: No. of Sows in Sept. 2007 sow2: No. of Sows in Dec. 2007 pig: No. of Pigs in Dec. 2006 goat: No. of Goats in Dec 2006 pop_regist: Village Population pop_migout: No. of Villagers as Migrant Workers age_vil: Ave. Villager Age edu_vil: Ave. Villager Education (Years) male_vil: Fraction Male in Village land: Land per Household (Mu) house_val: House Value (RMB yuan) lhouse_val: Log House Value surname_vil: No. of Surnames in the Village no_xnh: No. of Villagers in New Medical Coop. Scheme subsidy: No. of Households Receiving Gov. Subsidy insuredpig: No. of Insured Sows snow: No. of Sow Deaths in Snow Storm sow3: No. of Sows in March 2008 sow4: No. of Sows in June 2008 group: Coding for different groups (1 = Control Group; 2 = Low incentive Group; 3 = High Incentive Group) groupd1: Control Group Dummy groupd2: Low Incentive Group Dummy groupd3: High Incentive Dummy villageid: Village Id villagename: Village Name (in Chinese) snow_insured: No. of Sow Deaths in Snow Storm Interact with No. of Insured Sows towndummy1-26: 26 Town dummies sow2008: No. of sows at the end of 2008 sow2009: No. of sows at the end of 2009 sow2010: No. of sows at the end of 2010 sow2011: No. of sows at the end of 2011 pig2008: No. of pigs at the end of 2008 pig2009: No. of pigs at the end of 2009 pig2010: No. of pigs at the end of 2010 pig2011: No. of pigs at the end of 2011 sheep2008: No. of sheep at the end of 2008 cow2008: No. of cows at the end of 2008 sow200810: Average sows 2008-2010 pig200810: Average pigs 2008-2010 lnhouse: Log House Value insured_house: Insured Sows _ Log House Value insured_edu: Insured Sows _ Avg. Villager Education snow_house: Sow Deaths _ Log House Value snow_edu: Sow Deaths _ Avg. Villager Education insured_house_snow: insured_house _ snow insured_edu_snow: insured_edu _ snow house_groupd2: Log House Value _ Low Incentive Dummy house_groupd3: Log House Value _ High Incentive Dummy house_snow_gd2: snow_house _ Low Incentive Dummy house_snow_gd3: snow_house _ High Incentive Dummy edu_groupd2: Avg. Villager Education _ Low Incentive Dummy edu_groupd3: Avg. Villager Education _ High Incentive Dummy edu_snow_gd2: insured_edu _ snow _ Low Incentive Dummy edu_snow_gd3: insured_edu _ snow _ High Incentive Dummy

    file e3249019db8a6d5cd72c88756b60b22681a35b469f71e420f6e49bfa747360da · frame —
  2. 1Turno do modelo
    call_594942d3naive-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": "cai_effect_microinsurance",
        "x": [
          "insuredpig"
        ],
        "y": "sow3"
      }
  3. 2Ferramenta
    infer.olsv0.1.0call_68c19b7fdecidido pelo modeloreexecutável

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

    Argumentos
    {
      "cluster": null,
      "controls": [],
      "dataset": "cai_effect_microinsurance",
      "fe": [],
      "intercept": true,
      "level": 0.95,
      "se": "HC1",
      "subset": null,
      "weights": null,
      "x": [
        "insuredpig"
      ],
      "y": "sow3"
    }
    Figuras call_68c19b7f
    ChaveValorValor brutoErro-padrãoIC
    coef.insuredpig
    Coefficient on insuredpig in the regression of sow3
    1,21528 1.21528316472062840,07823810.07823808837359851,06136 – 1,36921
    Diagnósticos
    • n_obs = 321321
    • k = 22
    • r2 = 0,6679870.6679869119932953
    • df_resid = 319319
    • se_type = "HC1"
    • n_clusters = —
    Tabelas (1)
    {
      "coefficients": [
        {
          "ci_high": 14.504356602233493,
          "ci_low": 7.774434244163739,
          "coef": 11.139395423198616,
          "p": 2.865103404778542e-10,
          "se": 1.7103347707272123,
          "t": 6.512991265717114,
          "term": "Intercept"
        },
        {
          "ci_high": 1.3692110007238225,
          "ci_low": 1.0613553287174342,
          "coef": 1.2152831647206284,
          "p": 6.53231895830894e-41,
          "se": 0.0782380883735985,
          "t": 15.53313980420215,
          "term": "insuredpig"
        }
      ]
    }
    • 159 rows were dropped because one of 'sow3', 'insuredpig' was missing
    Digests dos dados
    cai_effect_microinsurance cdd6961e1779d8abe1e6f2ed9404f07595871ef43fe70583c3308e396368b57a
    sha256 do resultado
    c3cbaff3b363bc3cc925ceb7f0242ec06acc10c8450200edf997a3508b8b0277
  4. 3Turno do modelo
    call_357382a3naive-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.insuredpig = 1,215.",
        "caveats": [],
        "figure_refs": [
          "call_68c19b7f:coef.insuredpig"
        ]
      }
  5. 4Guarda
    resposta aceita
    Números encontrados
    1,215
    Sem lastro
    —
    Não citados
    —
    1 nova(s) tentativa(s) restante(s)
  6. 5Resposta

    Estimativa ingênua (infer.ols): coef.insuredpig = 1,215.

    Figuras citadas: call_68c19b7f:coef.insuredpig · Parada: answered
    Figura da resposta: call_68c19b7f:coef.insuredpig = 1,215281.2152831647206284 (se 0,07823810.0782380883735985)
  7. 6Pontuação
    #0resposta com lastropassouobrigatóriaevery number backed
  8. 7Pontuação
    #1figura da resposta próximafalhouobrigatória1.215283165 vs 0.904

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.

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