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Tarefa crb_papers.q027

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

Veredito

falhouDecidida pela verificação 1: figura da resposta próxima, fora da tolerância.
0.1644150377 in [-0.01748169035, 0.1643179209]?

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

Pergunta feita ao analista

Among districts observed in 2006 and 2008, how did the share of general-election contributions from Asian donors change when a district went from having no Asian major-party nominee to having one, relative to districts with no Asian nominee in either election?

Resposta

Estimativa ingênua (infer.ols): coef.cand_A_all = 0,1644.

Valores esperados e de onde vêm

crb_papers.q027.gold

Fonte
CausalReasoningBenchmark, causal_queries.json, index 27 (revision edd0fe1c1d1c)
Tipo
benchmark_gold
Localização
causal_queries.json['27'].effect and .standard_error
Trecho
index 27: effect 0.0734181, standard_error 0.0463775
Valor como publicado
0.07341811525625276
Erro-padrão como publicado
0.04637745183951293
Valor
0,07341810.07341811525625276 (se 0,04637750.04637745183951293)
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: 0,07341810.07341811525625276 · relativa 0.000001 · absoluta 4.637745183951293e-8
  • #3 figura da resposta próxima: 0,04637750.04637745183951293 · relativa 0.000001 · absoluta 4.637745183951293e-8

crb_papers.q027.script

Fonte
CausalReasoningBenchmark reference estimation script for index 27
Tipo
software
Localização
solutions/research_papers/DiD/Grumbach_Sahn_2020/question_2/estimation.py
Valor
—
Conferido por
arquivo do benchmark · sha256 of the script's 2327 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
d10cf356a88d55976922eea89a21d6669b6c4913a77c6eff7e30d703092409cf (identifica a cópia lida, não um arquivo canônico)
Endereço
https://huggingface.co/datasets/syrgkanislab/CausalReasoningBenchmark/resolve/edd0fe1c1d1c5c0a4f0e3e67a50481ac679fb888/solutions/research_papers/DiD/Grumbach_Sahn_2020/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
    0.1644150377 in [-0.01748169035, 0.1643179209]?
    obrigatóriafalhou[-0.017481690349192586,0.1643179208616981]0,1644150.16441503765836066interval · [-0.017481690349192586, 0.1643179208616981]—call_de30e4e7
    2figura da resposta próxima
    0.1644150377 vs 0.07341811525625276
    registradafalhou0,07341810.073418115256252760,1644150.16441503765836066relativa 0.000001 · absoluta 4.637745183951293e-8crb_papers.q027.goldcall_de30e4e7
    3figura da resposta próxima
    0.01751072842 vs 0.04637745183951293
    registradafalhou0,04637750.046377451839512930,01751070.01751072841515231relativa 0.000001 · absoluta 4.637745183951293e-8crb_papers.q027.goldcall_de30e4e7

    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_de30e4e7)igual
    Argumentos da referência e do agente
    ArgumentoReferênciaAgente
    cluster"district_final"—diferente
    controls["Treated","Post","cand_B_all","cand_H_all"][]diferente
    dataset"ref_panel""ds_0014"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"]["cand_A_all"]diferente
    y"general_sharetotal_A_all""general_sharetotal_A_all"igual

    Trajetória

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

    1. 0Início

      Among districts observed in 2006 and 2008, how did the share of general-election contributions from Asian donors change when a district went from having no Asian major-party nominee to having one, relative to districts with no Asian nominee in either election?

      ds_0014 (agent)

      | Variable Name | Description | | --- | --- | | cycle | Even-numbered year of the U.S. House general-election cycle (range: 1980–2012). Serves as the time index for the panel. | | district_final | Congressional district identifier (state postal abbreviation + district number, e.g., AK1 for at‑large). District codes are reindexed after each decennial redistricting to reflect new boundaries. | | general_sharetotal_A_all | Proportion (0–1) of all itemized individual dollars contributed to candidates in the district’s general-election contest that are attributed to donors classified as Asian. Donor ethnoracial identity is inferred with a surname–geography Bayesian method and aggregated to the district–cycle level. | | general_sharetotal_B_all | Proportion (0–1) of all itemized individual dollars contributed to candidates in the district’s general-election contest that are attributed to donors classified as Black. Donor ethnoracial identity is inferred with a surname–geography Bayesian method and aggregated to the district–cycle level. | | general_sharetotal_H_all | Proportion (0–1) of all itemized individual dollars contributed to candidates in the district’s general-election contest that are attributed to donors classified as Latino/Hispanic. Donor ethnoracial identity is inferred with a surname–geography Bayesian method and aggregated to the district–cycle level. | | cand_A_all | Indicator (1/0) that at least one major‑party nominee (Democratic or Republican) in the district’s general election is identified as Asian, based on verified public sources and candidate records. | | cand_B_all | Indicator (1/0) that at least one major‑party nominee in the district’s general election is identified as Black, based on verified public sources and candidate records. | | cand_H_all | Indicator (1/0) that at least one major‑party nominee in the district’s general election is identified as Latino/Hispanic, based on verified public sources and candidate records. | | dfe | Constant column equal to 1 for all rows, included for convenience in some analysis workflows; it carries no substantive information about districts or elections. | Dataset Context: This panel assembles district–election year information for U.S. House general elections from 1980 through 2012. Monetary figures come from the DIME compilation of Federal Election Commission disclosures and related sources, limited to itemized individual contributions reported under federal law (generally contributions exceeding the cycle-level reporting threshold). Dollars are aggregated to the general-election period for candidates competing in a given district and cycle. To characterize the ethnoracial composition of contributors, the data employ a widely used Bayesian surname–geography approach (implemented via the wru methodology). This procedure combines U.S. Census surname frequencies with donors’ geocoded locations (Census tract or, if unavailable, state) to estimate the probability that a donor belongs to an ethnoracial group; donors are then assigned to the most probable category, and contributions are summed to compute district-level shares for Asian, Black, and Latino/Hispanic contributors. These shares are fractions of total itemized individual dollars given to general-election candidates in the district; the remainder of contributions reflects donors in other categories. Candidate ethnoracial identity is coded from verified public records, including congressional caucus memberships and other documentation, and linked to each district’s major‑party general-election nominees. District identifiers reflect changes from decennial redistricting (e.g., around 1982, 1992, 2002, and 2012), so the same geographic area may appear under different labels across redistricting periods. Because federal disclosures only require itemization above the reporting threshold, very small donations are not included; all shares and totals therefore describe the universe of reported itemized individual contributions.

      file a9a7b57b90e4f8ca919421bfb08da2403ef1dd534ec517c0236f12c81f504038 · frame —
      ref_complete (reference)
      file a9a7b57b90e4f8ca919421bfb08da2403ef1dd534ec517c0236f12c81f504038 · frame —
      1. select {"columns":["cycle","district_final","cand_A_all","cand_B_all","cand_H_all","general_sharetotal_A_all"]}
      2. filter {"query":"cycle in [2006, 2008]"}
      3. dropna {"columns":["general_sharetotal_A_all"]}
      4. collapse {"aggs":[{"column":"general_sharetotal_A_all","fn":"count","name":"n"}],"by":["district_final","cycle"]}
      5. collapse {"aggs":[{"column":"n","fn":"count","name":"ncycles"}],"by":["district_final"]}
      ref_panel (reference)
      file a9a7b57b90e4f8ca919421bfb08da2403ef1dd534ec517c0236f12c81f504038 · frame —
      1. select {"columns":["cycle","district_final","cand_A_all","cand_B_all","cand_H_all","general_sharetotal_A_all"]}
      2. filter {"query":"cycle in [2006, 2008]"}
      3. merge {"how":"inner","on":["district_final"],"with":"ref_pre"}
      4. merge {"how":"inner","on":["district_final"],"with":"ref_post"}
      5. filter {"query":"a_pre == 0 and (a_post == 1 or a_post == 0)"}
      6. merge {"how":"left","on":["district_final"],"with":"ref_complete"}
      7. filter {"query":"ncycles == 2"}
      8. derive {"args":{"cmp":"eq","column":"a_post","value":1},"name":"Treated","op":"indicator"}
      9. derive {"args":{"cmp":"eq","column":"cycle","value":2008},"name":"Post","op":"indicator"}
      10. derive {"args":{"columns":["Treated","Post"]},"name":"Treated_Post","op":"interaction"}
      ref_post (reference)
      file a9a7b57b90e4f8ca919421bfb08da2403ef1dd534ec517c0236f12c81f504038 · frame —
      1. select {"columns":["cycle","district_final","cand_A_all","cand_B_all","cand_H_all","general_sharetotal_A_all"]}
      2. filter {"query":"cycle == 2008"}
      3. select {"columns":["district_final","cand_A_all"]}
      4. rename {"map":{"cand_A_all":"a_post"}}
      ref_pre (reference)
      file a9a7b57b90e4f8ca919421bfb08da2403ef1dd534ec517c0236f12c81f504038 · frame —
      1. select {"columns":["cycle","district_final","cand_A_all","cand_B_all","cand_H_all","general_sharetotal_A_all"]}
      2. filter {"query":"cycle == 2006"}
      3. select {"columns":["district_final","cand_A_all"]}
      4. rename {"map":{"cand_A_all":"a_pre"}}
    2. 1Turno do modelo
      call_9d287e76naive-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_0014",
          "x": [
            "cand_A_all"
          ],
          "y": "general_sharetotal_A_all"
        }
    3. 2Ferramenta
      infer.olsv0.1.0call_de30e4e7decidido pelo modeloreexecutável

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

      Argumentos
      {
        "cluster": null,
        "controls": [],
        "dataset": "ds_0014",
        "fe": [],
        "intercept": true,
        "level": 0.95,
        "se": "HC1",
        "subset": null,
        "weights": null,
        "x": [
          "cand_A_all"
        ],
        "y": "general_sharetotal_A_all"
      }
      Figuras call_de30e4e7
      ChaveValorValor brutoErro-padrãoIC
      coef.cand_A_all
      Coefficient on cand_A_all in the regression of general_sharetotal_A_all
      0,164415 0.164415037658360660,01751070.017510728415152310,130089 – 0,198742
      Diagnósticos
      • n_obs = 6.8476847
      • k = 22
      • r2 = 0,1251050.12510517461768478
      • df_resid = 6.8456845
      • se_type = "HC1"
      • n_clusters = —
      Tabelas (1)
      {
        "coefficients": [
          {
            "ci_high": 0.02144064089787409,
            "ci_low": 0.018991784476697,
            "coef": 0.020216212687285545,
            "p": 5.728129175800669e-214,
            "se": 0.0006246092845643672,
            "t": 32.36617384159653,
            "term": "Intercept"
          },
          {
            "ci_high": 0.19874150444218486,
            "ci_low": 0.13008857087453646,
            "coef": 0.16441503765836066,
            "p": 8.0476886375706e-21,
            "se": 0.01751072841515231,
            "t": 9.38938882268825,
            "term": "cand_A_all"
          }
        ]
      }
      • 160 rows were dropped because one of 'general_sharetotal_A_all', 'cand_A_all' was missing
      Digests dos dados
      ds_0014 3acd08e209f7c890895690c5876245b3df34de3f743239ee43ada9ec17fde6eb
      sha256 do resultado
      8cbb1b461e1fb2ceab2274b3d0120c81ff041f591f42443ab7666f1dc4b5bf4d
    4. 3Turno do modelo
      call_d6e52704naive-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.cand_A_all = 0,1644.",
          "caveats": [],
          "figure_refs": [
            "call_de30e4e7:coef.cand_A_all"
          ]
        }
    5. 4Guarda
      resposta aceita
      Números encontrados
      0,1644
      Sem lastro
      —
      Não citados
      —
      1 nova(s) tentativa(s) restante(s)
    6. 5Resposta

      Estimativa ingênua (infer.ols): coef.cand_A_all = 0,1644.

      Figuras citadas: call_de30e4e7:coef.cand_A_all · Parada: answered
      Figura da resposta: call_de30e4e7:coef.cand_A_all = 0,1644150.16441503765836066 (se 0,01751070.01751072841515231)
    7. 6Pontuação
      #0resposta com lastropassouobrigatóriaevery number backed
    8. 7Pontuação
      #1figura da resposta próximafalhouobrigatória0.1644150377 in [-0.01748169035, 0.1643179209]?
    9. 8Pontuação
      #2figura da resposta próximafalhouregistrada0.1644150377 vs 0.07341811525625276
    10. 9Pontuação
      #3figura da resposta próximafalhouregistrada0.01751072842 vs 0.04637745183951293

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