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Tarefa causcibench_real.q137

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 does providing information about how masks protect others increase the likelihood that someone would wear a mask?

Resposta

Estimativa (infer.ols): coef.T_mask_protectother = 0,1358.

Valores esperados e de onde vêm

causcibench_real.gold.q137

Fonte
CauSciBench real split, data/metadata_json/real_input.json at d9bb43a, id 137
Tipo
benchmark_gold
Localização
real_input.json, record id 137: fields effect and std_error
Trecho
id 137 effect 0.138 std_error 0.046
Valor como publicado
0.138
Erro-padrão como publicado
0.046
Valor
0,1380.138 (se 0,0460.046)
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: 0,1380.138 · 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
0.1358017395 vs 0.138
obrigatóriapassou0,1380.1380,1358020.1358017395063231relativa 0.05causcibench_real.gold.q137call_48f9e66b

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_48f9e66b)igual
Argumentos da referência e do agente
ArgumentoReferênciaAgente
cluster——não fixado pela referência
controls["pool_self_scenario_atm","pool_self_scenario_meet","age","female","lucid_hhi","lucid_incmissing","ethnicity","education","workstatus","fluvaccine","partisanship_1","partisanship_2","partisanship_3","partisanship_5","partisanship_6","partisanship_7"]["pool_self_scenario_atm","pool_self_scenario_meet","age","female","lucid_hhi","lucid_incmissing","ethnicity","education","workstatus","fluvaccine","partisanship_1","partisanship_2","partisanship_3","partisanship_5","partisanship_6","partisanship_7"]igual
dataset"bokemper_experimental_evidence""bokemper_experimental_evidence"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["T_mask_protectother"]["T_mask_protectother"]igual
y"pool_self""pool_self"igual

Trajetória

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

  1. 0Início

    How much does providing information about how masks protect others increase the likelihood that someone would wear a mask?

    bokemper_experimental_evidence (agent)

    The randomized experiment examines the causal relationship between beliefs, social norms, and reported intentions to engage in mask promoting behavior. Subjects are randomly assigned to a control group, a ""Mask Protects You"" group, and a ""Mask Protects Others"" group. The control group reads a message unrelated to the experiment, the ""Mask Protects You"" group reads a message about how masks protect the wearer, and the ""Mask Protects Others"" group reads a message about how masks protect others. After the treatment, subjects are asked about their beliefs about masks. They then read three vignette scenarios, taking place at an ATM, in a park or in a meeting in their neighborhood. In the first scenario, they are asked about what they would do when they have forgotten to bring their mask. In the second scenario, they are asked about what they would do if someone else was not wearing their mask properly. In the third scenario, they are asked about their evaluation of someone who took action against someone not wearing their mask properly. Variables in the dataset: age: age of the subject female: whether the subject is female lucid_hhi: household income lucid_incmissing: whether the subject's income is missing ethnicity: ethnicity of the subject education: education level of the subject workstatus: work status of the subject fluvaccine: whether the subject has received the flu vaccine pool_self_scenario_atm: whether the first vignette scenario takes place at an ATM pool_self_scenario_meet: whether the first vignette scenario takes place in a meeting in the neighborhood pool_other_scenario_atm: whether the second vignette scenario takes place at an ATM pool_other_scenario_meet: whether the second vignette scenario takes place in a meeting in the neighborhood partisanship_1: whether the subject is a strong democrat partisanship_2: whether the subject is a democrat partisanship_3: whether the subject is a democrat lean partisanship_4: whether the subject is an independent partisanship_5: whether the subject is a republican lean partisanship_6: whether the subject is a republican partisanship_7: whether the subject is a strong republican T_mask_protectyou: whether the subject was in the ""Mask Protects You"" group T_mask_protectother: whether the subject was in the ""Mask Protects Others"" group mskblfs_prtctothers: whether the subject believes that masks protect others after the intervention mskblfs_prtctyou: whether the subject believes that masks protect the wearer after the intervention pool_self: own mask wearing behavior in the vignette scenario, 1 means they would continue the activity without a mask, 2 means they would continue the activity without a mask while keeping distance from others, 3 means they would abandon the activity, 4 means they would go retrieve their mask and then continue the activity pool_other: behavior when someone else is not wearing their mask properly, 1 means they would continue the activity as normal, 2 means they would continue the activity as normal while keeping distance from others, 3 means they would abandon the activity, 4 means they would ask the other person to wear the mask properly pool_self_others: whether others are wearing masks in the first vignette scenario pool_other_others: whether others are wearing masks in the second vignette scenario

    file e94fe873cb4358fa045bc38ef25d976d69a12ed882c571b03327e0a5b7be7723 · frame —
  2. 1Turno do modelo
    call_bd3c76b3referenceFim: 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": [
          "pool_self_scenario_atm",
          "pool_self_scenario_meet",
          "age",
          "female",
          "lucid_hhi",
          "lucid_incmissing",
          "ethnicity",
          "education",
          "workstatus",
          "fluvaccine",
          "partisanship_1",
          "partisanship_2",
          "partisanship_3",
          "partisanship_5",
          "partisanship_6",
          "partisanship_7"
        ],
        "dataset": "bokemper_experimental_evidence",
        "x": [
          "T_mask_protectother"
        ],
        "y": "pool_self"
      }
  3. 2Ferramenta
    infer.olsv0.1.0call_48f9e66bdecidido pelo modeloreexecutável

    Motivo: causcibench_metadata_rule/1: ols

    Argumentos
    {
      "cluster": null,
      "controls": [
        "pool_self_scenario_atm",
        "pool_self_scenario_meet",
        "age",
        "female",
        "lucid_hhi",
        "lucid_incmissing",
        "ethnicity",
        "education",
        "workstatus",
        "fluvaccine",
        "partisanship_1",
        "partisanship_2",
        "partisanship_3",
        "partisanship_5",
        "partisanship_6",
        "partisanship_7"
      ],
      "dataset": "bokemper_experimental_evidence",
      "fe": [],
      "intercept": true,
      "level": 0.95,
      "se": "HC1",
      "subset": null,
      "weights": null,
      "x": [
        "T_mask_protectother"
      ],
      "y": "pool_self"
    }
    Figuras call_48f9e66b
    ChaveValorValor brutoErro-padrãoIC
    coef.T_mask_protectother
    Coefficient on T_mask_protectother in the regression of pool_self
    0,135802 0.13580173950632310,04030160.040301580974849070,0567786 – 0,214825
    Diagnósticos
    • n_obs = 2.8722872
    • k = 1818
    • r2 = 0,12410.12410044110109952
    • df_resid = 2.8542854
    • se_type = "HC1"
    • n_clusters = —
    Tabelas (1)
    {
      "coefficients": [
        {
          "ci_high": 2.948756482677905,
          "ci_low": 2.576049713399903,
          "coef": 2.762403098038904,
          "p": 6.323439935328254e-163,
          "se": 0.09503968200491043,
          "t": 29.06578641431248,
          "term": "Intercept"
        },
        {
          "ci_high": 0.2148248997540379,
          "ci_low": 0.0567785792586083,
          "coef": 0.1358017395063231,
          "p": 0.000762669890807,
          "se": 0.04030158097484907,
          "t": 3.3696380196864393,
          "term": "T_mask_protectother"
        },
        {
          "ci_high": -0.357833710127862,
          "ci_low": -0.5525452335302224,
          "coef": -0.45518947182904224,
          "p": 8.990275347077924e-20,
          "se": 0.04965115418402528,
          "t": -9.16775207565053,
          "term": "pool_self_scenario_atm"
        },
        {
          "ci_high": 0.17919189750640507,
          "ci_low": -0.003827049545512315,
          "coef": 0.08768242398044639,
          "p": 0.060374682694103544,
          "se": 0.04666956428610916,
          "t": 1.8787924276067087,
          "term": "pool_self_scenario_meet"
        },
        {
          "ci_high": 0.01209695562714288,
          "ci_low": 0.007475292092804281,
          "coef": 0.009786123859973581,
          "p": 1.53785976271917e-16,
          "se": 0.0011785174535147785,
          "t": 8.303758107941219,
          "term": "age"
        },
        {
          "ci_high": 0.0237027924644529,
          "ci_low": -0.1356429650568416,
          "coef": -0.05597008629619434,
          "p": 0.16847927699358214,
          "se": 0.040632935519234845,
          "t": -1.3774561345610679,
          "term": "female"
        },
        {
          "ci_high": 0.0072575164951763395,
          "ci_low": -0.005130046957065025,
          "coef": 0.0010637347690556575,
          "p": 0.736328862133323,
          "se": 0.0031588105941765554,
          "t": 0.33675167831104286,
          "term": "lucid_hhi"
        },
        {
          "ci_high": 0.1919181448839585,
          "ci_low": -0.21111681001031432,
          "coef": -0.009599332563177897,
          "p": 0.925589996754011,
          "se": 0.10277332505716825,
          "t": -0.09340295799360596,
          "term": "lucid_incmissing"
        },
        {
          "ci_high": 0.08923982287398263,
          "ci_low": -0.0009169202025746045,
          "coef": 0.04416145133570401,
          "p": 0.054842340583496685,
          "se": 0.02298983785347668,
          "t": 1.9209118227437003,
          "term": "ethnicity"
        },
        {
          "ci_high": 0.02966406880543104,
          "ci_low": -0.028755447346898524,
          "coef": 0.0004543107292662581,
          "p": 0.9756728057550792,
          "se": 0.014896891324924567,
          "t": 0.03049701574355538,
          "term": "education"
        },
        {
          "ci_high": 0.011793526099610402,
          "ci_low": -0.05242760683858204,
          "coef": -0.02031704036948582,
          "p": 0.2148417913782146,
          "se": 0.01637629513481749,
          "t": -1.2406371650135901,
          "term": "workstatus"
        },
        {
          "ci_high": 0.04909189115340912,
          "ci_low": 0.011312916039139202,
          "coef": 0.03020240359627416,
          "p": 0.0017353258568042578,
          "se": 0.009633583495913063,
          "t": 3.135116191092046,
          "term": "fluvaccine"
        },
        {
          "ci_high": 0.20213621332177767,
          "ci_low": -0.04981055891243731,
          "coef": 0.07616282720467017,
          "p": 0.23592389557211668,
          "se": 0.0642460590712879,
          "t": 1.1854863676565646,
          "term": "partisanship_1"
        },
        {
          "ci_high": 0.2745623797878153,
          "ci_low": -0.007858372168011912,
          "coef": 0.1333520038099017,
          "p": 0.06417542865495908,
          "se": 0.07201687940754484,
          "t": 1.8516770638624909,
          "term": "partisanship_2"
        },
        {
          "ci_high": 0.32311870586486957,
          "ci_low": 0.008829716684232797,
          "coef": 0.16597421127455117,
          "p": 0.03845131309476207,
          "se": 0.08014323337146709,
          "t": 2.0709697412039025,
          "term": "partisanship_3"
        },
        {
          "ci_high": -0.06421854752961217,
          "ci_low": -0.4362055483119409,
          "coef": -0.25021204792077656,
          "p": 0.008389740477395517,
          "se": 0.09485614208939333,
          "t": -2.637805443162282,
          "term": "partisanship_5"
        },
        {
          "ci_high": 0.0707610746716401,
          "ci_low": -0.2471140140522165,
          "coef": -0.08817646969028821,
          "p": 0.27676479789245356,
          "se": 0.08105768351919529,
          "t": -1.0878237060575153,
          "term": "partisanship_6"
        },
        {
          "ci_high": -0.333711479388898,
          "ci_low": -0.6147114846624447,
          "coef": -0.47421148202567137,
          "p": 4.3317750894487975e-11,
          "se": 0.07165459107788814,
          "t": -6.618019514063043,
          "term": "partisanship_7"
        }
      ]
    }
    • 228 rows were dropped because one of 'pool_self', 'T_mask_protectother', 'pool_self_scenario_atm', 'pool_self_scenario_meet', 'age', 'female', 'lucid_hhi', 'lucid_incmissing', 'ethnicity', 'education', 'workstatus', 'fluvaccine', 'partisanship_1', 'partisanship_2', 'partisanship_3', 'partisanship_5', 'partisanship_6', 'partisanship_7' was missing
    Digests dos dados
    bokemper_experimental_evidence e2a4c2f6a5449108e627246309dda3cbfe09d0576c9c98eb7dc615e7e153447d
    sha256 do resultado
    b51a7178eac00e3511b71ee3834ec235a0079fc4b8b4e446e9321e1649489d85
  4. 3Turno do modelo
    call_642295cbreferenceFim: 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.T_mask_protectother = 0,1358.",
        "caveats": [],
        "figure_refs": [
          "call_48f9e66b:coef.T_mask_protectother"
        ]
      }
  5. 4Guarda
    resposta aceita
    Números encontrados
    0,1358
    Sem lastro
    —
    Não citados
    —
    1 nova(s) tentativa(s) restante(s)
  6. 5Resposta

    Estimativa (infer.ols): coef.T_mask_protectother = 0,1358.

    Figuras citadas: call_48f9e66b:coef.T_mask_protectother · Parada: answered
    Figura da resposta: call_48f9e66b:coef.T_mask_protectother = 0,1358020.1358017395063231 (se 0,04030160.04030158097484907)
  7. 6Pontuação
    #0resposta com lastropassouobrigatóriaevery number backed
  8. 7Pontuação
    #1figura da resposta próximapassouobrigatória0.1358017395 vs 0.138

Épocas × verificações

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

Tentativas

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