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Tarefa crb_papers.q114

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

What is the effect of serving a Senate term on the probability of ever serving on a corporate board, for candidates whose service is determined by election outcomes?

Resposta

Estimativa ingênua (infer.ols): coef.everwon = 0,3427.

Valores esperados e de onde vêm

crb_papers.q114.gold

Fonte
CausalReasoningBenchmark, causal_queries.json, index 114 (revision edd0fe1c1d1c)
Tipo
benchmark_gold
Localização
causal_queries.json['114'].effect and .standard_error
Trecho
index 114: effect 0.327655, standard_error 0.185873
Valor como publicado
0.3276553502201477
Erro-padrão como publicado
0.1858726681755152
Valor
0,3276550.3276553502201477 (se 0,1858730.1858726681755152)
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: 0,3276550.3276553502201477 · relativa 0.000001 · absoluta 1.858726681755152e-7
  • #3 figura da resposta próxima: 0,1858730.1858726681755152 · relativa 0.000001 · absoluta 1.858726681755152e-7

crb_papers.q114.script

Fonte
CausalReasoningBenchmark reference estimation script for index 114
Tipo
software
Localização
solutions/research_papers/RDD/palmer_schneer_2016a/question_1/estimation.py
Valor
—
Conferido por
arquivo do benchmark · sha256 of the script's 1768 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
8b7f60260dfd648790b66b66e9ced63af6407fde604c435da1085999fd166f22 (identifica a cópia lida, não um arquivo canônico)
Endereço
https://huggingface.co/datasets/syrgkanislab/CausalReasoningBenchmark/resolve/edd0fe1c1d1c5c0a4f0e3e67a50481ac679fb888/solutions/research_papers/RDD/palmer_schneer_2016a/question_1/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
    0.3426823049 in [-0.0366550794, 0.6919657798]?
    obrigatóriapassou[-0.03665507940386209,0.6919657798441574]0,3426820.3426823049464559interval · [-0.03665507940386209, 0.6919657798441574]—call_67b00546
    2figura da resposta próxima
    0.3426823049 vs 0.3276553502201477
    registradafalhou0,3276550.32765535022014770,3426820.3426823049464559relativa 0.000001 · absoluta 1.858726681755152e-7crb_papers.q114.goldcall_67b00546
    3figura da resposta próxima
    0.05100216484 vs 0.1858726681755152
    registradafalhou0,1858730.18587266817551520,05100220.0510021648362006relativa 0.000001 · absoluta 1.858726681755152e-7crb_papers.q114.goldcall_67b00546

    Caminho de referência e caminho do agente

    A chamada de referência ao lado da chamada que deu a resposta, argumento por argumento.

    Nenhum caminho de referência registrado para esta tarefa.

    Trajetória

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

    1. 0Início

      What is the effect of serving a Senate term on the probability of ever serving on a corporate board, for candidates whose service is determined by election outcomes?

      ds_0079 (agent)

      | Variable Name | Description | | --- | --- | | Unnamed: 0 | Row identifier for the candidate observation (index carried over from the source CSV; not a substantive variable). | | d_board0 | Binary indicator equal to 1 if the individual ever served as a director on the board of at least one publicly traded corporation observed in BoardEx during 2000–2013, excluding any board positions held before the candidate’s first Senate election; 0 otherwise. | | margin | Vote margin in the candidate’s first U.S. Senate general election used for the dataset, measured as (candidate vote share − 0.50). Units are vote-share proportion (e.g., 0.03 = 3 percentage points above 50%; −0.03 = 3 points below 50%). | | everwon | Binary indicator equal to 1 if the candidate ever went on to hold U.S. Senate office at any point (i.e., ultimately won a Senate election and served), and 0 otherwise. This can differ from the first-election result captured by `margin` (e.g., a candidate with a negative first-election `margin` may later win and thus have `everwon = 1`). | Dataset Context: This dataset is a candidate-level file focused on U.S. Senate contests. Each row corresponds to an individual candidate, keyed to the candidate’s first Senate election used in the dataset, and includes (i) an election vote margin for that first contest and (ii) information on whether the individual later appeared as a director on the board of a publicly traded firm during the BoardEx coverage period. Corporate board service outcomes are based on BoardEx’s historical database of publicly traded firm directorships, which compiles board membership information from public disclosures for firms traded on U.S. exchanges. The BoardEx coverage window used here spans 2000–2013. Election results used to construct the vote margin were drawn from official election returns and standard political data sources. The board-service outcome variables are constructed to exclude board positions held prior to the candidate’s first Senate election. The dataset also applies eligibility restrictions consistent with the underlying data availability window (e.g., board outcomes are only observed in 2000–2013) and excludes cases where board service cannot be meaningfully assessed (such as candidates who did not survive into the relevant observation window or other similar sample restrictions implemented in the source study).

      file 7c30113ed34649d0517845d711f5fa66adbcc9ae997bc77780dcbada92691254 · frame —
    2. 1Turno do modelo
      call_94c5825cnaive-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_0079",
          "x": [
            "everwon"
          ],
          "y": "d_board0"
        }
    3. 2Ferramenta
      infer.olsv0.1.0call_67b00546decidido pelo modeloreexecutável

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

      Argumentos
      {
        "cluster": null,
        "controls": [],
        "dataset": "ds_0079",
        "fe": [],
        "intercept": true,
        "level": 0.95,
        "se": "HC1",
        "subset": null,
        "weights": null,
        "x": [
          "everwon"
        ],
        "y": "d_board0"
      }
      Figuras call_67b00546
      ChaveValorValor brutoErro-padrãoIC
      coef.everwon
      Coefficient on everwon in the regression of d_board0
      0,342682 0.34268230494645590,05100220.05100216483620060,242436 – 0,442929
      Diagnósticos
      • n_obs = 429429
      • k = 22
      • r2 = 0,1336950.13369517354639238
      • df_resid = 427427
      • se_type = "HC1"
      • n_clusters = —
      Tabelas (1)
      {
        "coefficients": [
          {
            "ci_high": 0.16242209524681586,
            "ci_low": 0.0891502317972093,
            "coef": 0.12578616352201258,
            "p": 4.883914424386667e-11,
            "se": 0.018639163684953303,
            "t": 6.748487520583073,
            "term": "Intercept"
          },
          {
            "ci_high": 0.4429288535759197,
            "ci_low": 0.24243575631699207,
            "coef": 0.3426823049464559,
            "p": 5.867230542055249e-11,
            "se": 0.0510021648362006,
            "t": 6.718975675778079,
            "term": "everwon"
          }
        ]
      }
      Digests dos dados
      ds_0079 c73a3376b9d9bc36db221cdbfa92366c5cad696f4d515eab51da090693091c8d
      sha256 do resultado
      41411dc10f28ef735c22a75a30e127432ef7c4e284e61ad1fa883bc109c7ba1c
    4. 3Turno do modelo
      call_29a57b56naive-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.everwon = 0,3427.",
          "caveats": [],
          "figure_refs": [
            "call_67b00546:coef.everwon"
          ]
        }
    5. 4Guarda
      resposta aceita
      Números encontrados
      0,3427
      Sem lastro
      —
      Não citados
      —
      1 nova(s) tentativa(s) restante(s)
    6. 5Resposta

      Estimativa ingênua (infer.ols): coef.everwon = 0,3427.

      Figuras citadas: call_67b00546:coef.everwon · Parada: answered
      Figura da resposta: call_67b00546:coef.everwon = 0,3426820.3426823049464559 (se 0,05100220.0510021648362006)
    7. 6Pontuação
      #0resposta com lastropassouobrigatóriaevery number backed
    8. 7Pontuação
      #1figura da resposta próximapassouobrigatória0.3426823049 in [-0.0366550794, 0.6919657798]?
    9. 8Pontuação
      #2figura da resposta próximafalhouregistrada0.3426823049 vs 0.3276553502201477
    10. 9Pontuação
      #3figura da resposta próximafalhouregistrada0.05100216484 vs 0.1858726681755152

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