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Tarefa causcibench_real.q013

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

How much differently do Black legislators respond to out of districts email?

Resposta

Estimativa (infer.ols): coef.treat_out = -0,2672.

Valores esperados e de onde vêm

causcibench_real.gold.q013

Fonte
CauSciBench real split, data/metadata_json/real_input.json at d9bb43a, id 13
Tipo
benchmark_gold
Localização
real_input.json, record id 13: fields effect and std_error
Trecho
id 13 effect -0.276 std_error 0.013
Valor como publicado
-0.276
Erro-padrão como publicado
0.013
Valor
-0,276-0.276 (se 0,0130.013)
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: -0,276-0.276 · 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
-0.2672311246 vs -0.276
obrigatóriapassou-0,276-0.276-0,267231-0.2672311245850423relativa 0.05causcibench_real.gold.q013call_adc28ae8

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_adc28ae8)igual
Argumentos da referência e do agente
ArgumentoReferênciaAgente
cluster——não fixado pela referência
controls["leg_black","leg_democrat","south","leg_senator","blackpercent","black_medianhh","white_medianhh","statessquireindex","urbanpercent","nonblacknonwhite","totalpop"]["leg_black","leg_democrat","south","leg_senator","blackpercent","black_medianhh","white_medianhh","statessquireindex","urbanpercent","nonblacknonwhite","totalpop"]igual
dataset"broockman_black_politicians""broockman_black_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_out"]["treat_out"]igual
y"responded""responded"igual

Pendências do caminho de referência: needs_spec:interaction

Trajetória

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

  1. 0Início

    How much differently do Black legislators respond to out of districts email?

    broockman_black_politicians (agent)

    This data is from a randomized field experiment in which fictional emails, appearing to come from Black constituents, were sent to legislators across the United States. The purpose was to test whether being outside a legislator's district reduced the likelihood of receiving a response, and whether Black legislators were more likely to respond regardless of electoral incentives. The main focus is on differences in responsiveness based on the race of the legislator and the sender's eligibility to vote. Variables include: leg_black: 1 if the legislator is Black, 0 otherwise treat_out: 1 if the email was from an out-of-district sender, 0 otherwise responded: 1 if the legislator responded to the email, 0 otherwise totalpop: Population of the legislator's district medianhhincom: Median household income in the district black_medianhh: Median household income among Black residents in the district white_medianhh: Median household income among White residents in the district blackpercent: Percentage of the district's population that is Black statessquireindex: State legislative professionalism index (Squire index) nonblacknonwhite: 1 if the legislator is neither Black nor White, 0 otherwise urbanpercent: Percentage of the district that is urban leg_senator: 1 if the legislator is a senator, 0 otherwise leg_democrat: 1 if the legislator is a Democrat, 0 otherwise south: 1 if the legislator represents a Southern state, 0 otherwise

    file 414450c964cacbde724fd24906b61bb65232c0ae9c73a1bc021dbce1ea058022 · frame —
  2. 1Turno do modelo
    call_eb3c65ecreferenceFim: 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": [
          "leg_black",
          "leg_democrat",
          "south",
          "leg_senator",
          "blackpercent",
          "black_medianhh",
          "white_medianhh",
          "statessquireindex",
          "urbanpercent",
          "nonblacknonwhite",
          "totalpop"
        ],
        "dataset": "broockman_black_politicians",
        "x": [
          "treat_out"
        ],
        "y": "responded"
      }
  3. 2Ferramenta
    infer.olsv0.1.0call_adc28ae8decidido pelo modeloreexecutável

    Motivo: causcibench_metadata_rule/1: ols

    Argumentos
    {
      "cluster": null,
      "controls": [
        "leg_black",
        "leg_democrat",
        "south",
        "leg_senator",
        "blackpercent",
        "black_medianhh",
        "white_medianhh",
        "statessquireindex",
        "urbanpercent",
        "nonblacknonwhite",
        "totalpop"
      ],
      "dataset": "broockman_black_politicians",
      "fe": [],
      "intercept": true,
      "level": 0.95,
      "se": "HC1",
      "subset": null,
      "weights": null,
      "x": [
        "treat_out"
      ],
      "y": "responded"
    }
    Figuras call_adc28ae8
    ChaveValorValor brutoErro-padrãoIC
    coef.treat_out
    Coefficient on treat_out in the regression of responded
    -0,267231 -0.26723112458504230,0126130.01261301217487693-0,291958 – -0,242505
    Diagnósticos
    • n_obs = 5.5935593
    • k = 1313
    • r2 = 0,08951250.08951247549215935
    • df_resid = 5.5805580
    • se_type = "HC1"
    • n_clusters = —
    Tabelas (1)
    {
      "coefficients": [
        {
          "ci_high": 0.48837892998224314,
          "ci_low": 0.38768337474520703,
          "coef": 0.4380311523637251,
          "p": 1.2714603443366106e-63,
          "se": 0.02568254164730191,
          "t": 17.055599807028546,
          "term": "Intercept"
        },
        {
          "ci_high": -0.24250471156870912,
          "ci_low": -0.29195753760137544,
          "coef": -0.2672311245850423,
          "p": 6.911875053915033e-96,
          "se": 0.01261301217487693,
          "t": -21.186939398768143,
          "term": "treat_out"
        },
        {
          "ci_high": 0.02531981050144974,
          "ci_low": -0.12472458323946961,
          "coef": -0.049702386369009935,
          "p": 0.1940790865210647,
          "se": 0.03826903165809239,
          "t": -1.2987625820550346,
          "term": "leg_black"
        },
        {
          "ci_high": -0.02483088220920489,
          "ci_low": -0.07800364679614565,
          "coef": -0.05141726450267527,
          "p": 0.0001514218793190987,
          "se": 0.013561787685580083,
          "t": -3.791333834059796,
          "term": "leg_democrat"
        },
        {
          "ci_high": 0.027741401504178594,
          "ci_low": -0.03798279148815787,
          "coef": -0.005120694991989637,
          "p": 0.7600156561639032,
          "se": 0.016763046986409097,
          "t": -0.3054751917202953,
          "term": "south"
        },
        {
          "ci_high": 0.12098530704268856,
          "ci_low": 0.05735880384106013,
          "coef": 0.08917205544187434,
          "p": 4.081622860417621e-8,
          "se": 0.016228028282890748,
          "t": 5.4949408447783314,
          "term": "leg_senator"
        },
        {
          "ci_high": 0.21868762831031277,
          "ci_low": -0.044135419495201195,
          "coef": 0.08727610440755579,
          "p": 0.19297707465150193,
          "se": 0.06703338449494219,
          "t": 1.301979678709807,
          "term": "blackpercent"
        },
        {
          "ci_high": 0.013638766359236408,
          "ci_low": -0.014010408747909871,
          "coef": -0.00018582119433673108,
          "p": 0.9789788869718461,
          "se": 0.007051960630548766,
          "t": -0.02635028810736156,
          "term": "black_medianhh"
        },
        {
          "ci_high": 0.04155633334143796,
          "ci_low": 0.0005762089856204029,
          "coef": 0.021066271163529184,
          "p": 0.04389824586536693,
          "se": 0.010452037808445318,
          "t": 2.015518078829326,
          "term": "white_medianhh"
        },
        {
          "ci_high": 0.6283875560357884,
          "ci_low": 0.3468114165587721,
          "coef": 0.4875994862972803,
          "p": 1.2417738938837578e-11,
          "se": 0.071816386651644,
          "t": 6.78952964679849,
          "term": "statessquireindex"
        },
        {
          "ci_high": 0.059030077242813624,
          "ci_low": -0.032548278457154,
          "coef": 0.013240899392829813,
          "p": 0.5708133628583375,
          "se": 0.023357187203738528,
          "t": 0.5668875827098945,
          "term": "urbanpercent"
        },
        {
          "ci_high": 0.02433299949662531,
          "ci_low": -0.09491329006501581,
          "coef": -0.035290145284195246,
          "p": 0.24596482188488095,
          "se": 0.03041393228076323,
          "t": -1.1603282652968954,
          "term": "nonblacknonwhite"
        },
        {
          "ci_high": -0.0018651697086579221,
          "ci_low": -0.005297991404080102,
          "coef": -0.003581580556369012,
          "p": 0.00004361639910613996,
          "se": 0.0008755459558558036,
          "t": -4.090682542035376,
          "term": "totalpop"
        }
      ]
    }
    Digests dos dados
    broockman_black_politicians b96d5891f0e7beff3062ed222dc9d9eae8bf1955fa80fb0fd7318a05f0b198b0
    sha256 do resultado
    047f02c8d4eb55a0dcaad4670c353dc845fd3af9127878be18f9544a75efb40f
  4. 3Turno do modelo
    call_8ec750c5referenceFim: 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_out = -0,2672.",
        "caveats": [],
        "figure_refs": [
          "call_adc28ae8:coef.treat_out"
        ]
      }
  5. 4Guarda
    resposta aceita
    Números encontrados
    -0,2672
    Sem lastro
    —
    Não citados
    —
    1 nova(s) tentativa(s) restante(s)
  6. 5Resposta

    Estimativa (infer.ols): coef.treat_out = -0,2672.

    Figuras citadas: call_adc28ae8:coef.treat_out · Parada: answered
    Figura da resposta: call_adc28ae8:coef.treat_out = -0,267231-0.2672311245850423 (se 0,0126130.01261301217487693)
  7. 6Pontuação
    #0resposta com lastropassouobrigatóriaevery number backed
  8. 7Pontuação
    #1figura da resposta próximapassouobrigatória-0.2672311246 vs -0.276

Épocas × verificações

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

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

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