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Tarefa causcibench_real.q007

causcibench_realagentT2publicépoca 1, tentativa 1Execução: 20261015T171059Z-suite-causcibench_real-scripted-ded01d

Veredito

passouPassou em todas as verificações obrigatórias.

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

Pergunta feita ao analista

Does being an immigrant make it less likely to get an interview request?

Resposta

Estimativa (infer.ols): coef.immigrant = -0,08612.

Valores esperados e de onde vêm

causcibench_real.gold.q007

Fonte
CauSciBench real split, data/metadata_json/real_input.json at d9bb43a, id 7
Tipo
benchmark_gold
Localização
real_input.json, record id 7: fields effect and std_error
Trecho
id 7 effect -0.087 std_error 0.03
Valor como publicado
-0.087
Erro-padrão como publicado
0.03
Valor
-0,087-0.087 (se 0,030.03)
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: -0,087-0.087 · 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
-0.08611704629 vs -0.087
obrigatóriapassou-0,087-0.087-0,086117-0.08611704628931742relativa 0.05causcibench_real.gold.q007call_2300d1a6

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.olsChamada do agente: infer.ols (call_2300d1a6)igual
Argumentos da referência e do agente
ArgumentoReferênciaAgente
cluster——não fixado pela referência
controls["citizen","woman","religious","experience","skilledjob","time"]["citizen","woman","religious","experience","skilledjob","time"]igual
dataset"vernby_can_immigrants""vernby_can_immigrants"igual
fe—[]não fixado pela referência
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["immigrant"]["immigrant"]igual
y"invited""invited"igual

Trajetória

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

  1. 0Início

    Does being an immigrant make it less likely to get an interview request?

    vernby_can_immigrants (agent)

    The data comes from a randomized field experiment designed to assess whether a candidate's background influences their likelihood of receiving a job interview or offer. Researchers submitted fictitious job applications to restaurants and cafes across Sweden at random. The applications varied in terms of country of birth, gender, citizenship status, work experience, and religious activity. A positive response was defined as a job offer, interview invitation, or follow-up inquiry, while a negative response included any other reply or no response at all. Variables: name: Name of the candidate stad: City citizen: 1 if the candidate is a Swedish citizen, 0 otherwise religious: 1 if the candidate is religious, 0 otherwise experience: 1 if the candidate has work experience, 0 otherwise poland: 1 if the candidate was born in Poland, 0 otherwise iraq: 1 if the candidate was born in Iraq, 0 otherwise somalia: 1 if the candidate was born in Somalia, 0 otherwise skilledjob: 1 if the job is high-skilled, 0 otherwise woman: 1 if the candidate is a woman, 0 otherwise invited: 1 if the candidate received an interview or a job or a follow-up response, 0 otherwise city1, city2, city3, city4, city5, city6, city7: Dummy variables for the seven cities immigrant: 1 if the candidate is an immigrant (not born in Sweden), 0 otherwise time: Proportion of the applicant's life spent living in Sweden (scaled between 0 and 1)

    file 6b86133cb042567bcf438fed9ac968ded163b85e3608000503564ddb195541c6 · frame —
  2. 1Turno do modelo
    call_ac278bc1referenceFim: 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": [
          "citizen",
          "woman",
          "religious",
          "experience",
          "skilledjob",
          "time"
        ],
        "dataset": "vernby_can_immigrants",
        "x": [
          "immigrant"
        ],
        "y": "invited"
      }
  3. 2Ferramenta
    infer.olsv0.1.0call_2300d1a6decidido pelo modeloreexecutável

    Motivo: causcibench_metadata_rule/1: ols

    Argumentos
    {
      "cluster": null,
      "controls": [
        "citizen",
        "woman",
        "religious",
        "experience",
        "skilledjob",
        "time"
      ],
      "dataset": "vernby_can_immigrants",
      "fe": [],
      "intercept": true,
      "level": 0.95,
      "se": "HC1",
      "subset": null,
      "weights": null,
      "x": [
        "immigrant"
      ],
      "y": "invited"
    }
    Figuras call_2300d1a6
    ChaveValorValor brutoErro-padrãoIC
    coef.immigrant
    Coefficient on immigrant in the regression of invited
    -0,086117 -0.086117046289317420,03001230.030012326029052013-0,144988 – -0,027246
    Diagnósticos
    • n_obs = 1.4921492
    • k = 88
    • r2 = 0,06949650.06949647865468667
    • df_resid = 1.4841484
    • se_type = "HC1"
    • n_clusters = —
    Tabelas (1)
    {
      "coefficients": [
        {
          "ci_high": 0.0755214209536049,
          "ci_low": -0.08433776928369113,
          "coef": -0.004408174165043122,
          "p": 0.9138662296924795,
          "se": 0.04074789399922117,
          "t": -0.10818164406551606,
          "term": "Intercept"
        },
        {
          "ci_high": -0.02724595302209632,
          "ci_low": -0.14498813955653853,
          "coef": -0.08611704628931742,
          "p": 0.004170898305356206,
          "se": 0.030012326029052013,
          "t": -2.8693892704602733,
          "term": "immigrant"
        },
        {
          "ci_high": 0.04987784094925174,
          "ci_low": -0.016043714493829627,
          "coef": 0.016917063227711058,
          "p": 0.31420977224342733,
          "se": 0.016803316402836152,
          "t": 1.0067693080430067,
          "term": "citizen"
        },
        {
          "ci_high": 0.11251894133178282,
          "ci_low": 0.04821200940970115,
          "coef": 0.08036547537074198,
          "p": 0.000001048570614275135,
          "se": 0.01639175102467627,
          "t": 4.902799905254732,
          "term": "woman"
        },
        {
          "ci_high": 0.026543476724701934,
          "ci_low": -0.03822439814339393,
          "coef": -0.005840460709345997,
          "p": 0.7235621831826234,
          "se": 0.016509244765736727,
          "t": -0.35376910284032403,
          "term": "religious"
        },
        {
          "ci_high": 0.06657669986231174,
          "ci_low": -0.008891563226213164,
          "coef": 0.028842568318049287,
          "p": 0.13399645374607272,
          "se": 0.019236759426040768,
          "t": 1.4993465208597012,
          "term": "experience"
        },
        {
          "ci_high": 0.08440262177804539,
          "ci_low": -0.011199864025803079,
          "coef": 0.03660137887612116,
          "p": 0.13331809641533296,
          "se": 0.024368945894287408,
          "t": 1.5019680799858188,
          "term": "skilledjob"
        },
        {
          "ci_high": 1.3961064725181096,
          "ci_low": 0.7268180601095997,
          "coef": 1.0614622663138547,
          "p": 6.375446300637741e-10,
          "se": 0.17060072206825336,
          "t": 6.2219095760285725,
          "term": "time"
        }
      ]
    }
    Digests dos dados
    vernby_can_immigrants 321ceca56d7e15f0f480ef67fa412659353c33e94500351553713de75069879c
    sha256 do resultado
    e60b3602948d19d1f31e23b9e79810139b94afdaf9da97a7a7bb75e1678e2c2b
  4. 3Turno do modelo
    call_40253ed9referenceFim: 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.immigrant = -0,08612.",
        "caveats": [],
        "figure_refs": [
          "call_2300d1a6:coef.immigrant"
        ]
      }
  5. 4Guarda
    resposta aceita
    Números encontrados
    -0,08612
    Sem lastro
    —
    Não citados
    —
    1 nova(s) tentativa(s) restante(s)
  6. 5Resposta

    Estimativa (infer.ols): coef.immigrant = -0,08612.

    Figuras citadas: call_2300d1a6:coef.immigrant · Parada: answered
    Figura da resposta: call_2300d1a6:coef.immigrant = -0,086117-0.08611704628931742 (se 0,03001230.030012326029052013)
  7. 6Pontuação
    #0resposta com lastropassouobrigatóriaevery number backed
  8. 7Pontuação
    #1figura da resposta próximapassouobrigatória-0.08611704629 vs -0.087

Épocas × verificações

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

Tentativas

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

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