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Tarefa crb_papers.q048

crb_papersagentT2publicépoca 1, tentativa 1Execução: 20261015T171027Z-suite-crb_papers-naive-54c3f2

Veredito

falhouDecidida pela verificação 1: figura da resposta próxima, fora da tolerância.
-17.55037888 in [2.771131448, 13.58427796]?

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

Pergunta feita ao analista

How did primary-school enrollment (percent of ages 5-14) evolve after the onset of a country's first civil war in 1880 compared with countries that had not experienced civil war by 1890?

Resposta

Estimativa ingênua (infer.ols): coef.treatment = -17,55.

Valores esperados e de onde vêm

crb_papers.q048.gold

Fonte
CausalReasoningBenchmark, causal_queries.json, index 48 (revision edd0fe1c1d1c)
Tipo
benchmark_gold
Localização
causal_queries.json['48'].effect and .standard_error
Trecho
index 48: effect 8.1777, standard_error 2.75846
Valor como publicado
8.177704705132356
Erro-padrão como publicado
2.7584557435386707
Valor
8,17778.177704705132356 (se 2,758462.7584557435386707)
Conferido por
arquivo do benchmark · Read from the catalog at the pinned revision by scripts/freeze_crb_papers.py; the catalog states the value its estimation script prints.
Endereço
https://huggingface.co/datasets/syrgkanislab/CausalReasoningBenchmark/resolve/edd0fe1c1d1c5c0a4f0e3e67a50481ac679fb888/causal_queries.json
Licença
MIT
  • #2 figura da resposta próxima: 8,17778.177704705132356 · relativa 0.000001 · absoluta 0.0000027584557435386706
  • #3 figura da resposta próxima: 2,758462.7584557435386707 · relativa 0.000001 · absoluta 0.0000027584557435386706

crb_papers.q048.script

Fonte
CausalReasoningBenchmark reference estimation script for index 48
Tipo
software
Localização
solutions/research_papers/DiD/Paglayan_2022/question_2/estimation.py
Valor
—
Conferido por
arquivo do benchmark · sha256 of the script's 2036 bytes at the pinned revision; the reference path of the item is a transcription of this script into typed transforms and tool calls (never executed here).
Implementação
independente da ferramenta
sha256 da cópia lida
7d155799d1f67171a0d116c7b42d19b16133e7828c974a27021ab5c23219f73d (identifica a cópia lida, não um arquivo canônico)
Endereço
https://huggingface.co/datasets/syrgkanislab/CausalReasoningBenchmark/resolve/edd0fe1c1d1c5c0a4f0e3e67a50481ac679fb888/solutions/research_papers/DiD/Paglayan_2022/question_2/estimation.py
Licença
MIT

    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
    -17.55037888 in [2.771131448, 13.58427796]?
    obrigatóriafalhou[2.771131447796562,13.58427796246815]-17,5504-17.550378882565795interval · [2.771131447796562, 13.58427796246815]—call_1d93ab78
    2figura da resposta próxima
    -17.55037888 vs 8.177704705132356
    registradafalhou8,17778.177704705132356-17,5504-17.550378882565795relativa 0.000001 · absoluta 0.0000027584557435386706crb_papers.q048.goldcall_1d93ab78
    3figura da resposta próxima
    0.9925948735 vs 2.7584557435386707
    registradafalhou2,758462.75845574353867070,9925950.9925948734966048relativa 0.000001 · absoluta 0.0000027584557435386706crb_papers.q048.goldcall_1d93ab78

    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 · benchmark_script

    Referência: infer.olsChamada do agente: infer.ols (call_1d93ab78)igual
    Argumentos da referência e do agente
    ArgumentoReferênciaAgente
    cluster"Countryid"—diferente
    controls["Post","TreatCohort"][]diferente
    dataset"ref_panel""ds_0025"diferente
    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["Interaction"]["treatment"]diferente
    y"primratio""primratio"igual

    Trajetória

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

    1. 0Início

      How did primary-school enrollment (percent of ages 5-14) evolve after the onset of a country's first civil war in 1880 compared with countries that had not experienced civil war by 1890?

      ds_0025 (agent)

      | Variable Name | Description | | --- | --- | | year | Gregorian calendar year of observation. One record per country-year. Coverage varies by country but spans much of 1828–2015. | | Countryid | Numeric identifier for countries in the panel. Serves as a stable code to group observations by country across years. | | primratio | Annual primary-school enrollment rate, expressed as a percentage of the resident population ages 5–14 enrolled in primary school (public and, where sources report it, private). Series are compiled from historical administrative counts and harmonized to a common 5–14 denominator; values are continuous percentages (0–100). | | treatment | Civil-war exposure indicator at the country-year level. Coded 1 from the first year of the earliest intrastate war that directly involved the state onward, and 0 otherwise; war timing is drawn from the Correlates of War intrastate war records. | Dataset Context: This is an annual country–year panel that brings together long-run measures of primary-school enrollment coverage and the timing of internal armed conflict for European and Latin American countries. The schooling series were constructed from historical administrative sources, anchored in International Historical Statistics and cross-checked against widely used decennial benchmarks, then extended backward with national statistical yearbooks, education reports compiled by the U.S. Bureau of Education (1872–1915), and regional data handbooks. The resulting enrollment measure reflects the share of children ages 5–14 enrolled in primary school each year and is available far earlier than most modern datasets, enabling analysis across the nineteenth, twentieth, and early twenty-first centuries. Civil conflict timing is drawn from the Correlates of War intrastate war dataset, focusing on episodes where the central state was a combatant. For each country, the panel flags the first qualifying episode and then maintains an “ever-exposed since onset” indicator thereafter, aligning conflict timing with the annual schooling series. Because the coverage begins as early as the 1820s for some countries and extends to 2015 for others, the panel spans periods of early state involvement in schooling, subsequent expansion and standardization of primary education, and the modern era of administrative reporting. Start years and continuity differ by country, reflecting when governments first recorded enrollment statistics and when national primary education systems took form. The dataset is suitable for historical description of how primary education coverage evolved over time in relation to episodes of internal conflict documented in comparative records.

      file c4a713c165661449eafadc60e8646b778b01b8c8bff567f77bc6adaa39ba2fb0 · frame —
      ref_adopt (reference)
      file c4a713c165661449eafadc60e8646b778b01b8c8bff567f77bc6adaa39ba2fb0 · frame —
      1. select {"columns":["year","Countryid","primratio","treatment"]}
      2. dropna {"columns":["year","Countryid","primratio","treatment"]}
      3. filter {"query":"treatment == 1"}
      4. collapse {"aggs":[{"column":"year","fn":"min","name":"adopt"}],"by":["Countryid"]}
      ref_panel (reference)
      file c4a713c165661449eafadc60e8646b778b01b8c8bff567f77bc6adaa39ba2fb0 · frame —
      1. select {"columns":["year","Countryid","primratio","treatment"]}
      2. dropna {"columns":["year","Countryid","primratio","treatment"]}
      3. filter {"query":"year in [1879, 1890]"}
      4. merge {"how":"left","on":["Countryid"],"with":"ref_years"}
      5. merge {"how":"left","on":["Countryid"],"with":"ref_adopt"}
      6. merge {"how":"left","on":["Countryid"],"with":"ref_pre"}
      7. merge {"how":"left","on":["Countryid"],"with":"ref_post"}
      8. filter {"query":"nyears == 2 and (adopt == 1880 or (t_pre == 0 and t_post == 0))"}
      9. derive {"args":{"cmp":"eq","column":"adopt","value":1880},"name":"TreatCohort","op":"indicator"}
      10. derive {"args":{"column":"TreatCohort","default":0,"keep_unmapped":false,"mapping":[{"source":1,"target":1}]},"name":"TreatCohort","op":"map"}
      11. derive {"args":{"cmp":"eq","column":"year","value":1890},"name":"Post","op":"indicator"}
      12. derive {"args":{"columns":["TreatCohort","Post"]},"name":"Interaction","op":"interaction"}
      ref_post (reference)
      file c4a713c165661449eafadc60e8646b778b01b8c8bff567f77bc6adaa39ba2fb0 · frame —
      1. select {"columns":["year","Countryid","primratio","treatment"]}
      2. dropna {"columns":["year","Countryid","primratio","treatment"]}
      3. filter {"query":"year == 1890"}
      4. select {"columns":["Countryid","treatment"]}
      5. rename {"map":{"treatment":"t_post"}}
      ref_pre (reference)
      file c4a713c165661449eafadc60e8646b778b01b8c8bff567f77bc6adaa39ba2fb0 · frame —
      1. select {"columns":["year","Countryid","primratio","treatment"]}
      2. dropna {"columns":["year","Countryid","primratio","treatment"]}
      3. filter {"query":"year == 1879"}
      4. select {"columns":["Countryid","treatment"]}
      5. rename {"map":{"treatment":"t_pre"}}
      ref_years (reference)
      file c4a713c165661449eafadc60e8646b778b01b8c8bff567f77bc6adaa39ba2fb0 · frame —
      1. select {"columns":["year","Countryid","primratio","treatment"]}
      2. dropna {"columns":["year","Countryid","primratio","treatment"]}
      3. filter {"query":"year in [1879, 1890]"}
      4. collapse {"aggs":[{"column":"primratio","fn":"count","name":"n"}],"by":["Countryid","year"]}
      5. collapse {"aggs":[{"column":"n","fn":"count","name":"nyears"}],"by":["Countryid"]}
    2. 1Turno do modelo
      call_df74f2e6naive-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": "ds_0025",
          "x": [
            "treatment"
          ],
          "y": "primratio"
        }
    3. 2Ferramenta
      infer.olsv0.1.0call_1d93ab78decidido pelo modeloreexecutável

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

      Argumentos
      {
        "cluster": null,
        "controls": [],
        "dataset": "ds_0025",
        "fe": [],
        "intercept": true,
        "level": 0.95,
        "se": "HC1",
        "subset": null,
        "weights": null,
        "x": [
          "treatment"
        ],
        "y": "primratio"
      }
      Figuras call_1d93ab78
      ChaveValorValor brutoErro-padrãoIC
      coef.treatment
      Coefficient on treatment in the regression of primratio
      -17,5504 -17.5503788825657950,9925950.9925948734966048-19,4966 – -15,6041
      Diagnósticos
      • n_obs = 2.8822882
      • k = 22
      • r2 = 0,1045450.1045449983798501
      • df_resid = 2.8802880
      • se_type = "HC1"
      • n_clusters = —
      Tabelas (1)
      {
        "coefficients": [
          {
            "ci_high": 58.539044168286665,
            "ci_low": 57.023321539844204,
            "coef": 57.781182854065435,
            "p": 0,
            "se": 0.38650853765883864,
            "t": 149.49523030993802,
            "term": "Intercept"
          },
          {
            "ci_high": -15.604110735750918,
            "ci_low": -19.49664702938067,
            "coef": -17.550378882565795,
            "p": 1.6859417632989326e-66,
            "se": 0.9925948734966048,
            "t": -17.681311228962162,
            "term": "treatment"
          }
        ]
      }
      • 134 rows were dropped because one of 'primratio', 'treatment' was missing
      Digests dos dados
      ds_0025 d6c08e9f9b08cd1155de7b527c9af22fac9ce1dae7c682c7d69358972c0c553b
      sha256 do resultado
      f1bc0d27ea0a307f7d0a4ebe4f13f0e5fbe4500087beae502f7ae238e2c7b55a
    4. 3Turno do modelo
      call_3d52dc0fnaive-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.treatment = -17,55.",
          "caveats": [],
          "figure_refs": [
            "call_1d93ab78:coef.treatment"
          ]
        }
    5. 4Guarda
      resposta aceita
      Números encontrados
      -17,55
      Sem lastro
      —
      Não citados
      —
      1 nova(s) tentativa(s) restante(s)
    6. 5Resposta

      Estimativa ingênua (infer.ols): coef.treatment = -17,55.

      Figuras citadas: call_1d93ab78:coef.treatment · Parada: answered
      Figura da resposta: call_1d93ab78:coef.treatment = -17,5504-17.550378882565795 (se 0,9925950.9925948734966048)
    7. 6Pontuação
      #0resposta com lastropassouobrigatóriaevery number backed
    8. 7Pontuação
      #1figura da resposta próximafalhouobrigatória-17.55037888 in [2.771131448, 13.58427796]?
    9. 8Pontuação
      #2figura da resposta próximafalhouregistrada-17.55037888 vs 8.177704705132356
    10. 9Pontuação
      #3figura da resposta próximafalhouregistrada0.9925948735 vs 2.7584557435386707

    Épocas × verificações

    Resultado de cada verificação em cada época pontuada
    #0123
    Época 1passoufalhoufalhoufalhou

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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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