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Tarefa causcibench_real.q158

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

Veredito

falhouDecidida pela verificação 1: figura da resposta próxima, fora da tolerância.
-0.01782423895 vs 0.019

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

Pergunta feita ao analista

Do legislators respond differently by race when a Democratic partisan signal is included?

Resposta

Estimativa (infer.ols): coef.treat_deshawn = -0,01782.

Valores esperados e de onde vêm

causcibench_real.gold.q158

Fonte
CauSciBench real split, data/metadata_json/real_input.json at d9bb43a, id 158
Tipo
benchmark_gold
Localização
real_input.json, record id 158: fields effect and std_error
Trecho
id 158 effect 0.019 std_error 0.025
Valor como publicado
0.019
Erro-padrão como publicado
0.025
Valor
0,0190.019 (se 0,0250.025)
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: 0,0190.019 · 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
-0.01782423895 vs 0.019
obrigatóriafalhou0,0190.019-0,0178242-0.017824238945471624relativa 0.05causcibench_real.gold.q158call_4797fbce

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_4797fbce)igual
Argumentos da referência e do agente
ArgumentoReferênciaAgente
cluster——não fixado pela referência
controls[][]igual
dataset"butler_do_politicians""butler_do_politicians"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["treat_deshawn"]["treat_deshawn"]igual
y"reply_atall""reply_atall"igual

Pendências do caminho de referência: needs_spec:subgroup

Trajetória

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

  1. 0Início

    Do legislators respond differently by race when a Democratic partisan signal is included?

    butler_do_politicians (agent)

    This study utilizes randomized field experiments to examine the interactions between constituents and state legislators across the United States during October 2008. Legislators across multiple states were sent an email asking for help with the voter registration process for the primary election using black (DeShawn Jackson) or white alias names (Jake Mueller). The sender name, the legislature receiving the email, and the (hypothetical) party affiliation of the sender were randomly assigned. Not all letters included information on which political party's primary election the sender was interested in. Legislative offices were contacted through official email addresses posted on state government websites. The outcome of interest is whether the legislator responded by the November election date or not. Variable Descriptions: leg_party: Political party affiliation of the state legislator (R = Republican, D = Democrat) leg_republican: Binary indicator for legislators affiliated with the Republican Party leg_black: Binary indicator for legislators who identify as Black or African American leg_latino: Binary indicator for legislators who identify as Latino or Hispanic leg_white: Binary indicator for legislators who identify as white leg_notwhite: Binary indicator for legislators who identify as non-white leg_notblackotherminority: Binary indicator for legislators who identify as minorities other than Black (including Latino, Asian American, Native American, and Arab American legislators) reply_atall: Binary indicator for whether the legislative office provided any form of response to the constituent inquiry treat_deshawn: Binary indicator for communications associated with the name ""DeShawn Jackson"" treat_jake: Binary indicator for communications associated with the name ""Jake Mueller"" treat_group: Categorical variable indicating the specific combination of communication characteristics treat_demprimary: Binary indicator for communications that mentioned interest in Democratic primary elections treat_repprimary: Binary indicator for communications that mentioned interest in Republican primary elections treat_noprimary: Binary indicator for communications that did not specify any particular primary election interest treat_primary: Binary indicator for communications that included any reference to primary election participation (either Democratic or Republican)

    file da070187b161931eb60102008c9fe7907e4660438519fa9b9656a45d317d70df · frame —
  2. 1Turno do modelo
    call_d0946bb2referenceFim: 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": [],
        "dataset": "butler_do_politicians",
        "x": [
          "treat_deshawn"
        ],
        "y": "reply_atall"
      }
  3. 2Ferramenta
    infer.olsv0.1.0call_4797fbcedecidido pelo modeloreexecutável

    Motivo: causcibench_metadata_rule/1: ols

    Argumentos
    {
      "cluster": null,
      "controls": [],
      "dataset": "butler_do_politicians",
      "fe": [],
      "intercept": true,
      "level": 0.95,
      "se": "HC1",
      "subset": null,
      "weights": null,
      "x": [
        "treat_deshawn"
      ],
      "y": "reply_atall"
    }
    Figuras call_4797fbce
    ChaveValorValor brutoErro-padrãoIC
    coef.treat_deshawn
    Coefficient on treat_deshawn in the regression of reply_atall
    -0,0178242 -0.0178242389454716240,01422350.014223500324634713-0,0457087 – 0,0100603
    Diagnósticos
    • n_obs = 4.8594859
    • k = 22
    • r2 = 0,0003232240.00032322359701220105
    • df_resid = 4.8574857
    • se_type = "HC1"
    • n_clusters = —
    Tabelas (1)
    {
      "coefficients": [
        {
          "ci_high": 0.5939137015970791,
          "ci_low": 0.5545848586661871,
          "coef": 0.5742492801316331,
          "p": 0,
          "se": 0.01003055222763491,
          "t": 57.2500164596655,
          "term": "Intercept"
        },
        {
          "ci_high": 0.010060258213811346,
          "ci_low": -0.045708736104754594,
          "coef": -0.017824238945471624,
          "p": 0.21020992334012087,
          "se": 0.014223500324634713,
          "t": -1.253154184177894,
          "term": "treat_deshawn"
        }
      ]
    }
    Digests dos dados
    butler_do_politicians 0d32e2cb48fbabd40ab0ec59157724749089ca983ed6cba8ad1f10344a49f900
    sha256 do resultado
    7c7e57a1ab2c79c7b71d6dd21e88a92c621f8e2490554b59b74589fcb01152ac
  4. 3Turno do modelo
    call_7fd9229creferenceFim: 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.treat_deshawn = -0,01782.",
        "caveats": [],
        "figure_refs": [
          "call_4797fbce:coef.treat_deshawn"
        ]
      }
  5. 4Guarda
    resposta aceita
    Números encontrados
    -0,01782
    Sem lastro
    —
    Não citados
    —
    1 nova(s) tentativa(s) restante(s)
  6. 5Resposta

    Estimativa (infer.ols): coef.treat_deshawn = -0,01782.

    Figuras citadas: call_4797fbce:coef.treat_deshawn · Parada: answered
    Figura da resposta: call_4797fbce:coef.treat_deshawn = -0,0178242-0.017824238945471624 (se 0,01422350.014223500324634713)
  7. 6Pontuação
    #0resposta com lastropassouobrigatóriaevery number backed
  8. 7Pontuação
    #1figura da resposta próximafalhouobrigatória-0.01782423895 vs 0.019

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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