Pular para o conteúdo

Tarefa causcibench_real.q087

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 having a strategic partnership with China increase the total Chinese foreign aid a country receives during COVID-19?

Resposta

Estimativa (infer.ols): coef.str_partnership = 0,4799.

Valores esperados e de onde vêm

causcibench_real.gold.q087

Fonte
CauSciBench real split, data/metadata_json/real_input.json at d9bb43a, id 87
Tipo
benchmark_gold
Localização
real_input.json, record id 87: fields effect and std_error
Trecho
id 87 effect 0.477 std_error 0.122
Valor como publicado
0.477
Erro-padrão como publicado
0.122
Valor
0,4770.477 (se 0,1220.122)
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: 0,4770.477 · 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
0.479860291 vs 0.477
obrigatóriapassou0,4770.4770,479860.4798602909914369relativa 0.05causcibench_real.gold.q087call_555e513e

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_555e513e)igual
Argumentos da referência e do agente
ArgumentoReferênciaAgente
cluster——não fixado pela referência
controls["democracy","taiwan","agree_us","chinese_exports_log","total_deaths_log","gdp_per_capita_log"]["democracy","taiwan","agree_us","chinese_exports_log","total_deaths_log","gdp_per_capita_log"]igual
dataset"urdinez_china""urdinez_china"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["str_partnership"]["str_partnership"]igual
y"total_china_ln""total_china_ln"igual

Trajetória

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

  1. 0Início

    Does having a strategic partnership with China increase the total Chinese foreign aid a country receives during COVID-19?

    urdinez_china (agent)

    This dataset contains information on 537 donations across 33 Latin American and Caribbean countries between 11 February and 20 June 2020. It details China's and Taiwan's mask diplomacy, including donors, recipients, donated items, and the political determinants of aid. The data highlight the influence of strategic partnerships, the One China Policy, and regime type in allocation decisions. Data variables: cow_code: country code donor code: Donor code (1 = central government, 2 = provincial government, 3 = municipal government, 4 = university, 5 = enterprise, 6 = individual, 7 = foundation, 8 = other, 100 = Taiwan) amount_usd: Total value of donations (in millions) country: Name of the recipient country iso_code: iso_code of the country str_partnership: Partnership status with China (0 = No partnership, 1 = Partnership, 2 = Strategic Partnership, 3 = Comprehensive Strategic Partnership) taiwan: dummy variable that is 1 if the country has diplomatic relations with Taiwan gdp_per_capita_log: the logarithm of gdp per capita total_deaths_log: total deaths due to COVID-19 chinese_exports: total Chinese exports chinese_exports_log: logarithm of the total Chinese exports to the country democracy: Discrete variable for democracy status (0 = Autocracy; 1 = Anocracy; 2 = Weak democracy; 3 = Full democracy) amount_usd_log: logarithm of total donations customs: total customs customs_log: logarithm of total customs agree_us: Measure of affinity with the United States agree_cn: Measure of affinity with China total_china: Total donations from China (in millions) total_china_ln: Logarithm of total donations from China

    file f74c78cc3d6c50207291c4826731647739aeb407a77c31665ce9255c74deb420 · frame —
  2. 1Turno do modelo
    call_c116708creferenceFim: 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": [
          "democracy",
          "taiwan",
          "agree_us",
          "chinese_exports_log",
          "total_deaths_log",
          "gdp_per_capita_log"
        ],
        "dataset": "urdinez_china",
        "x": [
          "str_partnership"
        ],
        "y": "total_china_ln"
      }
  3. 2Ferramenta
    infer.olsv0.1.0call_555e513edecidido pelo modeloreexecutável

    Motivo: causcibench_metadata_rule/1: ols

    Argumentos
    {
      "cluster": null,
      "controls": [
        "democracy",
        "taiwan",
        "agree_us",
        "chinese_exports_log",
        "total_deaths_log",
        "gdp_per_capita_log"
      ],
      "dataset": "urdinez_china",
      "fe": [],
      "intercept": true,
      "level": 0.95,
      "se": "HC1",
      "subset": null,
      "weights": null,
      "x": [
        "str_partnership"
      ],
      "y": "total_china_ln"
    }
    Figuras call_555e513e
    ChaveValorValor brutoErro-padrãoIC
    coef.str_partnership
    Coefficient on str_partnership in the regression of total_china_ln
    0,47986 0.47986029099143690,04112960.041129627723671350,398865 – 0,560856
    Diagnósticos
    • n_obs = 264264
    • k = 88
    • r2 = 0,8032330.8032331306426179
    • df_resid = 256256
    • se_type = "HC1"
    • n_clusters = —
    Tabelas (1)
    {
      "coefficients": [
        {
          "ci_high": 1.4832780649830488,
          "ci_low": -0.28768607734138363,
          "coef": 0.5977959938208326,
          "p": 0.1848756210394685,
          "se": 0.4496490182566915,
          "t": 1.3294724764184147,
          "term": "Intercept"
        },
        {
          "ci_high": 0.5608557929589215,
          "ci_low": 0.3988647890239522,
          "coef": 0.4798602909914369,
          "p": 1.6641866086638344e-25,
          "se": 0.04112962772367135,
          "t": 11.667022473808164,
          "term": "str_partnership"
        },
        {
          "ci_high": -0.4793503984418121,
          "ci_low": -0.8038953947446383,
          "coef": -0.6416228965932252,
          "p": 1.7126788999431735e-13,
          "se": 0.08240219859909138,
          "t": -7.786477879247996,
          "term": "democracy"
        },
        {
          "ci_high": -0.6343627051724173,
          "ci_low": -0.8420121437897367,
          "coef": -0.738187424481077,
          "p": 1.8567620111987278e-33,
          "se": 0.05272233580815547,
          "t": -14.001417296213361,
          "term": "taiwan"
        },
        {
          "ci_high": 3.0431052453234155,
          "ci_low": 1.0442732260676377,
          "coef": 2.0436892356955267,
          "p": 0.00007450758329668867,
          "se": 0.5075048295098399,
          "t": 4.026935542011236,
          "term": "agree_us"
        },
        {
          "ci_high": -0.022586674675154322,
          "ci_low": -0.047335442058918833,
          "coef": -0.03496105836703658,
          "p": 6.64478429095237e-8,
          "se": 0.00628372912314692,
          "t": -5.563743707260239,
          "term": "chinese_exports_log"
        },
        {
          "ci_high": 0.13679347638300088,
          "ci_low": 0.06108791719099864,
          "coef": 0.09894069678699977,
          "p": 5.26802646688378e-7,
          "se": 0.019221693739421588,
          "t": 5.147345396731779,
          "term": "total_deaths_log"
        },
        {
          "ci_high": 0.1898211385389041,
          "ci_low": 0.018930418924878484,
          "coef": 0.10437577873189129,
          "p": 0.01685728632392524,
          "se": 0.043389271678177924,
          "t": 2.4055665074550157,
          "term": "gdp_per_capita_log"
        }
      ]
    }
    • 33 rows were dropped because one of 'total_china_ln', 'str_partnership', 'democracy', 'taiwan', 'agree_us', 'chinese_exports_log', 'total_deaths_log', 'gdp_per_capita_log' was missing
    Digests dos dados
    urdinez_china 1ad6a4f8dd4dac236433e58763ee29509e326480601d43cae0c5921dcf42e743
    sha256 do resultado
    cc3d7e2f61f121186279e75f37ca9c5faaf32ec5c231eddba7d3088d0bd29edd
  4. 3Turno do modelo
    call_c81680c7referenceFim: 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.str_partnership = 0,4799.",
        "caveats": [],
        "figure_refs": [
          "call_555e513e:coef.str_partnership"
        ]
      }
  5. 4Guarda
    resposta aceita
    Números encontrados
    0,4799
    Sem lastro
    —
    Não citados
    —
    1 nova(s) tentativa(s) restante(s)
  6. 5Resposta

    Estimativa (infer.ols): coef.str_partnership = 0,4799.

    Figuras citadas: call_555e513e:coef.str_partnership · Parada: answered
    Figura da resposta: call_555e513e:coef.str_partnership = 0,479860.4798602909914369 (se 0,04112960.04112962772367135)
  7. 6Pontuação
    #0resposta com lastropassouobrigatóriaevery number backed
  8. 7Pontuação
    #1figura da resposta próximapassouobrigatória0.479860291 vs 0.477

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.

Downloads desta tarefa

Pacote da tarefa (.zip)
100,0 KB · sha256 9647860ff97f2806d76ec05469f26f0a73c0e82ea9e1d514db9d12f0606d5fd7

O hash de um zip é só de transporte: o que ancora a integridade é o RUN_SHA256SUMS que vai dentro dele, cópia exata do SHA256SUMS da execução.

Downloads desta tarefa
ArquivoTamanhosha256
attachments/03bcdbbb4aa339ed1c3fffbcc60eed8306b48bafd1f939f144036b2e83b399f9.json1,8 KB03bcdbbb4aa339ed1c3fffbcc60eed8306b48bafd1f939f144036b2e83b399f9
attachments/433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420.json2,4 KB433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420
attachments/52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b.json64,0 KB52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b
attachments/58e39dfab70e37f1d4c4e7b0b3b549df3d36090772f05de346eecc9907b36f5e.json302 B58e39dfab70e37f1d4c4e7b0b3b549df3d36090772f05de346eecc9907b36f5e
attachments/8baf85dd2805ec78e4b325d1d2a51722a167e46761de4f685b92b0e43538ae2a.json1,9 KB8baf85dd2805ec78e4b325d1d2a51722a167e46761de4f685b92b0e43538ae2a
attachments/8f1f760d8057658bbb4f6d132d420fd2de71aadf08add4e0caed0f556d9f59bc.json146 B8f1f760d8057658bbb4f6d132d420fd2de71aadf08add4e0caed0f556d9f59bc
attachments/eb312f258473bd1227c25ef79ef36752dc8369b8ac22e6db71aef894850a935e.json2,4 KBeb312f258473bd1227c25ef79ef36752dc8369b8ac22e6db71aef894850a935e
native/causcibench_real/causcibench_real.q087.e1.a1.json1,9 KB4b374b5700a9e85d193eb8d55f0bec702ccff68b9b56eb902425fbe84a759dd6
sources/causcibench_real.gold.q087.json1,2 KB04845d9d752a53bd2c51e69e949d1c97a68affeb557e7230f80302100b4aa686
specs/causcibench_real.q087.json6,1 KB3581cd05a542fbfb69459f253ffa37bdbc7110a4d85870297a87a4f7ff022f92
traces/causcibench_real.q087.e1.a1.jsonl14,8 KB2c0f7bff39f9b0c5c9fa2457d2ad8cdf1b569ce81cfcd5a7dbdb3f773c0707bd
trajectories/causcibench_real.q087.e1.a1.jsonl11,5 KBaec0f72f5198cba2ba427eacc0879c1557b0cffff8b13d6ed6a33632ff20747c