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Tarefa causcibench_real.q114

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

Veredito

falhouDecidida pela verificação 1: figura da resposta próxima, fora da tolerância.
-0.004028343054 vs 0.065

Família do método (das chamadas de ferramentas): Regressão linear por mínimos quadrados ordinários (MQO) · infer.ols (ols · regressão linear)

Calculado pelo agente
coef.minorityprimarymargin =-0,00402834-0.004028343053599029
Coefficient on minorityprimarymargin in the regression of general2p
via infer.ols (call_68f2f32d)
Valor esperado (gabarito)
coef.minorityprimarymargin =0,0650.065
CauSciBench real split, data/metadata_json/real_input.json at d9bb43a, id 114
Como foi validado
1 de 2 verificações obrigatórias aprovadas
relativa 0.05

Pergunta feita ao analista

How does effect of nominating a minority candidate affect the general election vote share?

Resposta

Estimativa ingênua (infer.ols): coef.minorityprimarymargin = -0,004028.

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
    white_evaluating_the_minority— This dataset contains information on U.S. state legislative primary and general elections, focusing on competitive races where the top two primary candidates differed by race or ethnicity, enabling analysis of electoral outcomes when parties nominate candidates of different racial backgrounds. Variables: year.x: Election year of the contest general2p: Party's two-party vote share in the general election for that district-year (NA if the general was uncontested) general2palt: Alternative outcome that codes uncontested generals as 1 for the party's nominee; otherwise same as general2p minorityprimarymargin: Primary election win margin of the minority candidate calculated as minority candidate vote share minus white candidate vote share incumbentrunning: Indicator that the seat's incumbent entered the party primary (1 = incumbent ran; 0 = open seat) incumbentwins: Indicator that the incumbent won the party primary (1 = nominee is the seat's incumbent) womanwins: Indicator that the party's primary winner/nominee is a woman (1 = yes, 0 = no) party: Party of the contest (e.g., ""Democratic"", ""Republican"") Dem_2pyShare: District Democratic two-party presidential vote share (2016) - baseline partisan lean Total.x: District population uniquedist_noyear: District identifier without year (e.g., ""MA 0 TWELFTH SUFFOLK""; includes a chamber flag) uniquedist: Unique district-year identifier (e.g., ""2018 MA 0 Twelfth Suffolk"") pctWhite, pctBlack, pctLatino, pctAsian: Racial/ethnic composition of the district; share of residents who are White, Black, Latino, Asian, respectively RDDBlack: Indicator that the primary matchup was Black vs. White RDDLatino: Indicator that the primary matchup was Latino vs. White RDDAsian: Indicator that the primary matchup was Asian vs. White uncontestedgen: Indicator that the general election was uncontested for that party (1 = no major-party opponent) Upper.x: Chamber indicator (1 = upper chamber/senate; 0 = lower chamber/house) mmd2keep: Flag for multi-member districts (NA = single-member (kept), 1 = multi-member districts)
  2. 2Chamada que gerou a resposta finalinfer.ols(Regressão linear por mínimos quadrados ordinários (MQO))citada na resposta
    Ver chamada call_68f2f32d nos detalhes

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

    Argumentos principais
    dataset:
    "white_evaluating_the_minority"
    intercept:
    true
    level:
    0,950.95
    se:
    "HC1"
    x:
    ["minorityprimarymargin"]
    y:
    "general2p"
    Resultado calculado
    • coef.minorityprimarymargin = -0,00402834 -0.004028343053599029
      Coefficient on minorityprimarymargin in the regression of general2p
    Diagnósticos: n_obs = 400400 (observations used) · k = 22 (parameters estimated, fixed-effect dummies included) · r2 = 0,0000876380.0000876379749003009 · df_resid = 398398 · 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: -0,004028
    Figuras citadas: call_68f2f32d:coef.minorityprimarymargin
    • #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|

Aprofundamento técnico para especialistas

Acesso direto às fontes primárias, tabela formal de verificações, comparação de argumentos, trajetória completa com prompts brutos e somas SHA-256.

Valores esperados e de onde vêm

causcibench_real.gold.q114

Fonte
CauSciBench real split, data/metadata_json/real_input.json at d9bb43a, id 114
Tipo
benchmark_gold
Localização
real_input.json, record id 114: fields effect and std_error
Trecho
id 114 effect 0.065 std_error 0.037
Valor como publicado
0.065
Erro-padrão como publicado
0.037
Valor
0,0650.065 (se 0,0370.037)
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: 0,0650.065 · relativa 0.05
Especificação técnica do gabarito

CauSciBench real_input.json @ d9bb43a, id 114: effect 0.065

Verificações

Cada verificação, o que esperava, o que observou e a chamada que leu
#VerificaçãoObrigatóriaResultadoEsperadoObservadoTolerânciaFonteChamada
0
resposta com lastro
every number in the answer was stated by a tool call
every number backed
obrigatóriapassou0 sem lastro0 sem lastro[]———
1
figura da resposta próxima
CauSciBench effect accuracy: |answer figure − gold| ≤ 5% of |gold|
-0.004028343054 vs 0.065
obrigatóriafalhou0,0650.065-0,00402834-0.004028343053599029relativa 0.05causcibench_real.gold.q114call_68f2f32d

Caminho de referência e caminho do agente

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

Nenhum caminho de referência registrado para esta tarefa.

Trajetória

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

  1. 0Início

    How does effect of nominating a minority candidate affect the general election vote share?

    white_evaluating_the_minority (agent)

    This dataset contains information on U.S. state legislative primary and general elections, focusing on competitive races where the top two primary candidates differed by race or ethnicity, enabling analysis of electoral outcomes when parties nominate candidates of different racial backgrounds. Variables: year.x: Election year of the contest general2p: Party's two-party vote share in the general election for that district-year (NA if the general was uncontested) general2palt: Alternative outcome that codes uncontested generals as 1 for the party's nominee; otherwise same as general2p minorityprimarymargin: Primary election win margin of the minority candidate calculated as minority candidate vote share minus white candidate vote share incumbentrunning: Indicator that the seat's incumbent entered the party primary (1 = incumbent ran; 0 = open seat) incumbentwins: Indicator that the incumbent won the party primary (1 = nominee is the seat's incumbent) womanwins: Indicator that the party's primary winner/nominee is a woman (1 = yes, 0 = no) party: Party of the contest (e.g., ""Democratic"", ""Republican"") Dem_2pyShare: District Democratic two-party presidential vote share (2016) - baseline partisan lean Total.x: District population uniquedist_noyear: District identifier without year (e.g., ""MA 0 TWELFTH SUFFOLK""; includes a chamber flag) uniquedist: Unique district-year identifier (e.g., ""2018 MA 0 Twelfth Suffolk"") pctWhite, pctBlack, pctLatino, pctAsian: Racial/ethnic composition of the district; share of residents who are White, Black, Latino, Asian, respectively RDDBlack: Indicator that the primary matchup was Black vs. White RDDLatino: Indicator that the primary matchup was Latino vs. White RDDAsian: Indicator that the primary matchup was Asian vs. White uncontestedgen: Indicator that the general election was uncontested for that party (1 = no major-party opponent) Upper.x: Chamber indicator (1 = upper chamber/senate; 0 = lower chamber/house) mmd2keep: Flag for multi-member districts (NA = single-member (kept), 1 = multi-member districts)

    file acae1a36b9f4d0e127a3407e5312a7c6bc9790faefc40c09c9dc42bfecb6420e · frame —
  2. 1Turno do modelo
    call_7322bbd7naive-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": "white_evaluating_the_minority",
        "x": [
          "minorityprimarymargin"
        ],
        "y": "general2p"
      }
  3. 2Ferramenta
    infer.ols— Regressão linear por mínimos quadrados ordinários (MQO)v0.1.0call_68f2f32ddecidido pelo modeloreexecutável

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

    Argumentos
    {
      "cluster": null,
      "controls": [],
      "dataset": "white_evaluating_the_minority",
      "fe": [],
      "intercept": true,
      "level": 0.95,
      "se": "HC1",
      "subset": null,
      "weights": null,
      "x": [
        "minorityprimarymargin"
      ],
      "y": "general2p"
    }
    Figuras call_68f2f32d
    ChaveValorValor brutoErro-padrãoIC
    coef.minorityprimarymargin
    Coefficient on minorityprimarymargin in the regression of general2p
    -0,00402834-0.0040283430535990290,02127750.021277484268004514-0,0458586 – 0,037802
    Diagnósticos
    • n_obs = 400400 (observations used)
    • k = 22 (parameters estimated, fixed-effect dummies included)
    • r2 = 0,0000876380.0000876379749003009
    • df_resid = 398398
    • se_type = "HC1" (heteroskedasticity-robust standard errors (HC1))
    • n_clusters = — (errors not clustered)
    Derivação matemática e pressupostos
    coef.minorityprimarymargin
    1. Coeficiente de minorityprimarymargin na regressão por mínimos quadrados ordinários de general2p.→ -0,00402834 (-0.004028343053599029)
    2. Erro-padrão robusto à heterocedasticidade (HC1): a raiz da variância estimada do coeficiente.ep = √V_jj→ 0,0212775 (0.021277484268004514)
    3. Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 398 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": 0.5498509104609107,
          "ci_low": 0.5193737138118898,
          "coef": 0.5346123121364003,
          "p": 1.8322939590692953e-223,
          "se": 0.007751294704154544,
          "t": 68.97071167347804,
          "term": "Intercept"
        },
        {
          "ci_high": 0.037801963816610204,
          "ci_low": -0.04585864992380827,
          "coef": -0.004028343053599029,
          "p": 0.8499352262374987,
          "se": 0.021277484268004514,
          "t": -0.18932421722696557,
          "term": "minorityprimarymargin"
        }
      ]
    }
    • 175 rows were dropped because one of 'general2p', 'minorityprimarymargin' was missing
    Hashes SHA-256 e metadados de reprodução
    Digests dos dados
    white_evaluating_the_minority e69cd42bbe02ad196ac809276410dbb2da036c739107a88fd8bbb19f8abb08f4
    sha256 do resultado
    21374048012bcb6052ac400de66864e7d6984314a64068e9a88445c8e9752f57
  4. 3Turno do modelo
    call_565fe96enaive-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.minorityprimarymargin = -0,004028.",
        "caveats": [],
        "figure_refs": [
          "call_68f2f32d:coef.minorityprimarymargin"
        ]
      }
  5. 4Guarda
    resposta aceita
    Números encontrados
    -0,004028
    Sem lastro
    —
    Não citados
    —
    1 nova(s) tentativa(s) restante(s)
  6. 5Resposta

    Estimativa ingênua (infer.ols): coef.minorityprimarymargin = -0,004028.

    Figuras citadas: call_68f2f32d:coef.minorityprimarymargin · Parada: answered
    Figura da resposta: call_68f2f32d:coef.minorityprimarymargin = -0,00402834-0.004028343053599029 (se 0,02127750.021277484268004514)
  7. 6Pontuação
    #0resposta com lastropassouobrigatóriaevery number backed
  8. 7Pontuação
    #1figura da resposta próximafalhouobrigatória-0.004028343054 vs 0.065

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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