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Tarefa crb_papers.q033

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

Veredito

passouPassou em todas as verificações obrigatórias.

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

Pergunta feita ao analista

Did states that adopted direct primaries in 1912 experience an increase in the log of the state-average daily candidate newspaper advertisements (week before the election) from 1910 to 1912 relative to states that had not yet adopted?

Resposta

Estimativa ingênua (infer.ols): coef.prim_sw = 1,021.

Valores esperados e de onde vêm

crb_papers.q033.gold

Fonte
CausalReasoningBenchmark, causal_queries.json, index 33 (revision edd0fe1c1d1c)
Tipo
benchmark_gold
Localização
causal_queries.json['33'].effect and .standard_error
Trecho
index 33: effect 0.61347, standard_error 0.223833
Valor como publicado
0.61347038637508
Erro-padrão como publicado
0.22383298763880474
Valor
0,613470.61347038637508 (se 0,2238330.22383298763880474)
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: 0,613470.61347038637508 · relativa 0.000001 · absoluta 2.2383298763880474e-7
  • #3 figura da resposta próxima: 0,2238330.22383298763880474 · relativa 0.000001 · absoluta 2.2383298763880474e-7

crb_papers.q033.script

Fonte
CausalReasoningBenchmark reference estimation script for index 33
Tipo
software
Localização
solutions/research_papers/DiD/Hirano_etal_2022/question_2/estimation.py
Valor
—
Conferido por
arquivo do benchmark · sha256 of the script's 1742 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
21726561292a6bc9d1ceffe81ded25dedb9065022434ba3e8605839c9231e6d5 (identifica a cópia lida, não um arquivo canônico)
Endereço
https://huggingface.co/datasets/syrgkanislab/CausalReasoningBenchmark/resolve/edd0fe1c1d1c5c0a4f0e3e67a50481ac679fb888/solutions/research_papers/DiD/Hirano_etal_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
    1.020609492 in [0.1747577306, 1.052183042]?
    obrigatóriapassou[0.1747577306030227,1.0521830421471372]1,020611.0206094924859823interval · [0.1747577306030227, 1.0521830421471372]—call_dff064c4
    2figura da resposta próxima
    1.020609492 vs 0.61347038637508
    registradafalhou0,613470.613470386375081,020611.0206094924859823relativa 0.000001 · absoluta 2.2383298763880474e-7crb_papers.q033.goldcall_dff064c4
    3figura da resposta próxima
    0.04099967176 vs 0.22383298763880474
    registradafalhou0,2238330.223832987638804740,04099970.040999671762948706relativa 0.000001 · absoluta 2.2383298763880474e-7crb_papers.q033.goldcall_dff064c4

    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_dff064c4)igual
    Argumentos da referência e do agente
    ArgumentoReferênciaAgente
    cluster"state"—diferente
    controls["Treated","Post"][]diferente
    dataset"ref_panel""ds_0017"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["Treated_Post"]["prim_sw"]diferente
    y"ln_x_cand_all""ln_x_cand_all"igual

    Trajetória

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

    1. 0Início

      Did states that adopted direct primaries in 1912 experience an increase in the log of the state-average daily candidate newspaper advertisements (week before the election) from 1910 to 1912 relative to states that had not yet adopted?

      ds_0017 (agent)

      | Variable Name | Description | | --- | --- | | (index) | Row index from the CSV export; carries no substantive information. | | state | Two-letter U.S. state postal abbreviation identifying the state associated with the observation. | | year | Calendar year in which the state held the referenced general election. Most elections occurred in November of even-numbered years, though some states held major general elections in other months or in odd-numbered years; the value here reflects the actual calendar year of that election. | | ln_x_cand_all | Log-transformed average number of newspaper advertisements per day placed by or on behalf of individual candidates in the seven days preceding the general election; constructed as ln(ads per newspaper-day + 1). Source counts come from hand-coding full newspaper issues, then averaging ad counts to the newspaper-year and aggregating to a state-year mean. Included offices typically comprise statewide executive posts, state legislatures, U.S. House, judicial, and many local offices; advertisements focused solely on parties are tracked separately, and candidate ads for U.S. Senate and the presidency are excluded from this summary measure. | | prim_sw | Indicator for whether, in that year, the state used state-administered direct primary elections to nominate candidates for statewide offices (1 = direct primaries in use; 0 = nomination by caucus/convention or other non-primary procedures). Reflects the institutional status in each state-year during the late 19th and early 20th centuries. | Dataset Context: This state-year panel assembles historical evidence on campaign communications during an era when newspapers were the dominant mass medium. Researchers reviewed issues from 90 local newspapers across 33 mostly non-Southern states between 1880 and 1930, focusing on the week immediately preceding each state’s general election. Political advertisements were hand-coded at the ad level, distinguishing candidate-focused content from party-only notices. To make newspapers comparable despite different publication schedules and occasional missing issues, counts were normalized to advertisements per newspaper-day, averaged to the newspaper-year, and then aggregated to a state-year mean. The offices captured in the primary advertising measure are those commonly elected across states throughout the period: statewide executives (e.g., governor, attorney general), state legislatures, the U.S. House, judicial posts, and many local offices. Candidate advertising for U.S. Senate and for the presidency was excluded from that main summary to keep the measure focused on offices most consistently affected by state-level electoral rules of the time. The log transformation adds one before taking logs so that years with zero observed advertising are retained on a continuous scale. The panel spans a period of substantial institutional change often associated with Progressive Era reforms. States transitioned at different times to practices such as direct primary elections for nominating statewide candidates, and the dataset records whether a state employed direct primaries in each year. Because election timing varied across states—some held major contests in odd-numbered years or in months other than November—the year variable records the actual calendar year of the pertinent general election. Overall, the dataset provides a harmonized view of newspaper-based campaign activity across places and time, facilitating comparisons of advertising intensity during a formative phase in U.S. electoral development.

      file a1cd03905fff0a36614036650735de9568deb88305b615380f0334a23c7d923b · frame —
      ref_adopt (reference)
      file a1cd03905fff0a36614036650735de9568deb88305b615380f0334a23c7d923b · frame —
      1. select {"columns":["state","year","ln_x_cand_all","prim_sw"]}
      2. dropna {"columns":["state","year","ln_x_cand_all","prim_sw"]}
      3. filter {"query":"prim_sw == 1"}
      4. collapse {"aggs":[{"column":"year","fn":"min","name":"adopt_year"}],"by":["state"]}
      ref_panel (reference)
      file a1cd03905fff0a36614036650735de9568deb88305b615380f0334a23c7d923b · frame —
      1. select {"columns":["state","year","ln_x_cand_all","prim_sw"]}
      2. dropna {"columns":["state","year","ln_x_cand_all","prim_sw"]}
      3. filter {"query":"year in [1910, 1912]"}
      4. merge {"how":"left","on":["state"],"with":"ref_adopt"}
      5. merge {"how":"left","on":["state"],"with":"ref_years"}
      6. merge {"how":"inner","on":["state"],"with":"ref_pre"}
      7. merge {"how":"inner","on":["state"],"with":"ref_post"}
      8. filter {"query":"nyears == 2 and p_pre == 0 and ((adopt_year == 1912 and p_post == 1) or ((adopt_year != adopt_year or adopt_year > 1912) and p_post == 0))"}
      9. derive {"args":{"cmp":"eq","column":"p_post","value":1},"name":"Treated","op":"indicator"}
      10. derive {"args":{"cmp":"eq","column":"year","value":1912},"name":"Post","op":"indicator"}
      11. derive {"args":{"columns":["Treated","Post"]},"name":"Treated_Post","op":"interaction"}
      ref_post (reference)
      file a1cd03905fff0a36614036650735de9568deb88305b615380f0334a23c7d923b · frame —
      1. select {"columns":["state","year","ln_x_cand_all","prim_sw"]}
      2. dropna {"columns":["state","year","ln_x_cand_all","prim_sw"]}
      3. filter {"query":"year == 1912"}
      4. select {"columns":["state","prim_sw"]}
      5. rename {"map":{"prim_sw":"p_post"}}
      ref_pre (reference)
      file a1cd03905fff0a36614036650735de9568deb88305b615380f0334a23c7d923b · frame —
      1. select {"columns":["state","year","ln_x_cand_all","prim_sw"]}
      2. dropna {"columns":["state","year","ln_x_cand_all","prim_sw"]}
      3. filter {"query":"year == 1910"}
      4. select {"columns":["state","prim_sw"]}
      5. rename {"map":{"prim_sw":"p_pre"}}
      ref_years (reference)
      file a1cd03905fff0a36614036650735de9568deb88305b615380f0334a23c7d923b · frame —
      1. select {"columns":["state","year","ln_x_cand_all","prim_sw"]}
      2. dropna {"columns":["state","year","ln_x_cand_all","prim_sw"]}
      3. filter {"query":"year in [1910, 1912]"}
      4. collapse {"aggs":[{"column":"ln_x_cand_all","fn":"count","name":"n"}],"by":["state","year"]}
      5. collapse {"aggs":[{"column":"n","fn":"count","name":"nyears"}],"by":["state"]}
    2. 1Turno do modelo
      call_c88bd431naive-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_0017",
          "x": [
            "prim_sw"
          ],
          "y": "ln_x_cand_all"
        }
    3. 2Ferramenta
      infer.olsv0.1.0call_dff064c4decidido pelo modeloreexecutável

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

      Argumentos
      {
        "cluster": null,
        "controls": [],
        "dataset": "ds_0017",
        "fe": [],
        "intercept": true,
        "level": 0.95,
        "se": "HC1",
        "subset": null,
        "weights": null,
        "x": [
          "prim_sw"
        ],
        "y": "ln_x_cand_all"
      }
      Figuras call_dff064c4
      ChaveValorValor brutoErro-padrãoIC
      coef.prim_sw
      Coefficient on prim_sw in the regression of ln_x_cand_all
      1,02061 1.02060949248598230,04099970.0409996717629487060,940137 – 1,10108
      Diagnósticos
      • n_obs = 854854
      • k = 22
      • r2 = 0,4724140.47241425747224075
      • df_resid = 852852
      • se_type = "HC1"
      • n_clusters = —
      Tabelas (1)
      {
        "coefficients": [
          {
            "ci_high": 0.3089938421577919,
            "ci_low": 0.23606762503033543,
            "coef": 0.27253073359406366,
            "p": 1.3055500613185812e-43,
            "se": 0.01857754022834457,
            "t": 14.669904101634055,
            "term": "Intercept"
          },
          {
            "ci_high": 1.10108168978413,
            "ci_low": 0.9401372951878345,
            "coef": 1.0206094924859823,
            "p": 3.192319434678205e-103,
            "se": 0.040999671762948706,
            "t": 24.89311373971302,
            "term": "prim_sw"
          }
        ]
      }
      • 45 rows were dropped because one of 'ln_x_cand_all', 'prim_sw' was missing
      Digests dos dados
      ds_0017 f94064123b5fc42bd483d30636a96ea348e90e5edb4aebf0e6d1b71ed7d043e6
      sha256 do resultado
      7253ca9486b0d1843cd53ac22fb3831cea4ffc07c4d55fd7a36c6c359c580535
    4. 3Turno do modelo
      call_c6aa7486naive-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.prim_sw = 1,021.",
          "caveats": [],
          "figure_refs": [
            "call_dff064c4:coef.prim_sw"
          ]
        }
    5. 4Guarda
      resposta aceita
      Números encontrados
      1,021
      Sem lastro
      —
      Não citados
      —
      1 nova(s) tentativa(s) restante(s)
    6. 5Resposta

      Estimativa ingênua (infer.ols): coef.prim_sw = 1,021.

      Figuras citadas: call_dff064c4:coef.prim_sw · Parada: answered
      Figura da resposta: call_dff064c4:coef.prim_sw = 1,020611.0206094924859823 (se 0,04099970.040999671762948706)
    7. 6Pontuação
      #0resposta com lastropassouobrigatóriaevery number backed
    8. 7Pontuação
      #1figura da resposta próximapassouobrigatória1.020609492 in [0.1747577306, 1.052183042]?
    9. 8Pontuação
      #2figura da resposta próximafalhouregistrada1.020609492 vs 0.61347038637508
    10. 9Pontuação
      #3figura da resposta próximafalhouregistrada0.04099967176 vs 0.22383298763880474

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    #0123
    Época 1passoupassoufalhoufalhou

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