Tarefa crb_papers.q027

Veredito

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?

Como o agente resolveu e como foi validado

Resumo passo a passo do que o agente executou, por que o caminho está correto e como cada verificação conferiu o resultado.

1. 1Preparação da base de dados
   ds_0014— | 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.
   ref_complete
   ref_panel
   ref_post
   ref_pre
   Transformação "select" aplicada à base "ref_pre" (7007 → 7007 linhas). {"columns":["cycle","district_final","cand_A_all","cand_B_all","cand_H_all","general_sharetotal_A_all"]}
   Transformação "filter" aplicada à base "ref_pre" (7007 → 402 linhas). {"query":"cycle == 2006"}
   Transformação "select" aplicada à base "ref_pre" (402 → 402 linhas). {"columns":["district_final","cand_A_all"]}
   Transformação "rename" aplicada à base "ref_pre" (402 → 402 linhas). {"map":{"cand_A_all":"a_pre"}}
   Transformação "select" aplicada à base "ref_post" (7007 → 7007 linhas). {"columns":["cycle","district_final","cand_A_all","cand_B_all","cand_H_all","general_sharetotal_A_all"]}
   Transformação "filter" aplicada à base "ref_post" (7007 → 401 linhas). {"query":"cycle == 2008"}
   Transformação "select" aplicada à base "ref_post" (401 → 401 linhas). {"columns":["district_final","cand_A_all"]}
   Transformação "rename" aplicada à base "ref_post" (401 → 401 linhas). {"map":{"cand_A_all":"a_post"}}
   Transformação "select" aplicada à base "ref_complete" (7007 → 7007 linhas). {"columns":["cycle","district_final","cand_A_all","cand_B_all","cand_H_all","general_sharetotal_A_all"]}
   Transformação "filter" aplicada à base "ref_complete" (7007 → 803 linhas). {"query":"cycle in [2006, 2008]"}
   Transformação "dropna" aplicada à base "ref_complete" (803 → 799 linhas). {"columns":["general_sharetotal_A_all"]}
   Transformação "collapse" aplicada à base "ref_complete" (799 → 799 linhas). {"aggs":[{"column":"general_sharetotal_A_all","fn":"count","name":"n"}],"by":["district_final","cycle"]}
   Transformação "collapse" aplicada à base "ref_complete" (799 → 408 linhas). {"aggs":[{"column":"n","fn":"count","name":"ncycles"}],"by":["district_final"]}
   Transformação "select" aplicada à base "ref_panel" (7007 → 7007 linhas). {"columns":["cycle","district_final","cand_A_all","cand_B_all","cand_H_all","general_sharetotal_A_all"]}
   Transformação "filter" aplicada à base "ref_panel" (7007 → 803 linhas). {"query":"cycle in [2006, 2008]"}
   Transformação "merge" aplicada à base "ref_panel" (803 → 795 linhas). {"how":"inner","on":["district_final"],"with":"ref_pre"}
   Transformação "merge" aplicada à base "ref_panel" (795 → 786 linhas). {"how":"inner","on":["district_final"],"with":"ref_post"}
   Transformação "filter" aplicada à base "ref_panel" (786 → 772 linhas). {"query":"a_pre == 0 and (a_post == 1 or a_post == 0)"}
   Transformação "merge" aplicada à base "ref_panel" (772 → 772 linhas). {"how":"left","on":["district_final"],"with":"ref_complete"}
   Transformação "filter" aplicada à base "ref_panel" (772 → 768 linhas). {"query":"ncycles == 2"}
   Transformação "derive" aplicada à base "ref_panel" (768 → 768 linhas). {"args":{"cmp":"eq","column":"a_post","value":1},"name":"Treated","op":"indicator"}
   Transformação "derive" aplicada à base "ref_panel" (768 → 768 linhas). {"args":{"cmp":"eq","column":"cycle","value":2008},"name":"Post","op":"indicator"}
   Transformação "derive" aplicada à base "ref_panel" (768 → 768 linhas). {"args":{"columns":["Treated","Post"]},"name":"Treated_Post","op":"interaction"}
2. 2Chamada que gerou a resposta finalinfer.ols(Regressão linear por mínimos quadrados ordinários (MQO))citada na respostacoincide com a referência

   Ver chamada call_38dd916d nos detalhes

   smf.ols('general_sharetotal_A_all ~ Treated + Post + Treated:Post + cand_B_all + cand_H_all').fit(cov_type='cluster', groups=district_final) (CR1 errors with statsmodels' default G/(G-1)·(N-1)/(N-K) correction)

   Por que este é o caminho de referência: smf.ols('general_sharetotal_A_all ~ Treated + Post + Treated:Post + cand_B_all + cand_H_all').fit(cov_type='cluster', groups=district_final) (CR1 errors with statsmodels' default G/(G-1)·(N-1)/(N-K) correction)
   Argumentos principais

   cluster:

   "district_final"

   controls:

   ["Treated","Post","cand_B_all","cand_H_all"]

   dataset:

   "ref_panel"

   intercept:

   true

   level:

   0,950.95

   se:

   "HC1"

   x:

   ["Treated_Post"]

   y:

   "general_sharetotal_A_all"

   Resultado calculado

   • coef.Treated_Post = 0,0734181 0.0734181152562507
     Coefficient on Treated_Post in the regression of general_sharetotal_A_all
   Diagnósticos: n_obs = 768768 (observations used) · k = 66 (parameters estimated, fixed-effect dummies included) · r2 = 0,07941470.0794147497797385 · df_resid = 762762 · se_type = "cluster" (cluster-robust standard errors (CR1) by 'district_final') · n_clusters = 384384 (384 clusters of 'district_final')
3. 3Conferência de lastro e resposta final

   Todos os 1 números escritos na resposta foram conferidos contra as ferramentas e o enunciado (nenhum número sem lastro).

   Números encontrados: 0,07342
   Figuras citadas: call_38dd916d:coef.Treated_Post
   • ✓
     #0resposta com lastro

     every number traced to a tool call or the question
   • ✓
     #1figura da resposta próxima

     CRB estimate_within_solution_ci: the answer figure inside the gold 95% interval effect ± 1.96·|SE| of source crb_papers.q027.gold (closed, evaluate_result.py; no --auto-rescale)
   • ✓
     #2figura da resposta próxima

     replication cell: the gold effect to rel 1e-6 (abs floor 1e-6·SE); never required
   • ✓
     #3figura da resposta próxima

     replication cell: the gold SE to rel 1e-6; never required

Valores esperados e de onde vêm

Verificações

Caminho de referência e caminho do agente

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

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. 1Transformação
   ref_pre · select {"columns":["cycle","district_final","cand_A_all","cand_B_all","cand_H_all","general_sharetotal_A_all"]}

   7007 → 7007 linhas

   Frame antes → depois: 3acd08e209f7 → 72f6007995a7
3. 2Transformação
   ref_pre · filter {"query":"cycle == 2006"}

   7007 → 402 linhas

   Frame antes → depois: 72f6007995a7 → 395b6892d594
4. 3Transformação
   ref_pre · select {"columns":["district_final","cand_A_all"]}

   402 → 402 linhas

   Frame antes → depois: 395b6892d594 → 783350e0b591
5. 4Transformação
   ref_pre · rename {"map":{"cand_A_all":"a_pre"}}

   402 → 402 linhas

   Frame antes → depois: 783350e0b591 → e671dd574d7e
6. 5Transformação
   ref_post · select {"columns":["cycle","district_final","cand_A_all","cand_B_all","cand_H_all","general_sharetotal_A_all"]}

   7007 → 7007 linhas

   Frame antes → depois: 3acd08e209f7 → 72f6007995a7
7. 6Transformação
   ref_post · filter {"query":"cycle == 2008"}

   7007 → 401 linhas

   Frame antes → depois: 72f6007995a7 → f3d7fbac1b2e
8. 7Transformação
   ref_post · select {"columns":["district_final","cand_A_all"]}

   401 → 401 linhas

   Frame antes → depois: f3d7fbac1b2e → 59de7e69fc23
9. 8Transformação
   ref_post · rename {"map":{"cand_A_all":"a_post"}}

   401 → 401 linhas

   Frame antes → depois: 59de7e69fc23 → bc712b8fd448
10. 9Transformação
    ref_complete · select {"columns":["cycle","district_final","cand_A_all","cand_B_all","cand_H_all","general_sharetotal_A_all"]}

    7007 → 7007 linhas

    Frame antes → depois: 3acd08e209f7 → 72f6007995a7
11. 10Transformação
    ref_complete · filter {"query":"cycle in [2006, 2008]"}

    7007 → 803 linhas

    Frame antes → depois: 72f6007995a7 → 00c546098379
12. 11Transformação
    ref_complete · dropna {"columns":["general_sharetotal_A_all"]}

    803 → 799 linhas

    Frame antes → depois: 00c546098379 → 1105554ae239
13. 12Transformação
    ref_complete · collapse {"aggs":[{"column":"general_sharetotal_A_all","fn":"count","name":"n"}],"by":["district_final","cycle"]}

    799 → 799 linhas

    Frame antes → depois: 1105554ae239 → ca2a89c133af
14. 13Transformação
    ref_complete · collapse {"aggs":[{"column":"n","fn":"count","name":"ncycles"}],"by":["district_final"]}

    799 → 408 linhas

    Frame antes → depois: ca2a89c133af → 5d5e8fcc5940
15. 14Transformação
    ref_panel · select {"columns":["cycle","district_final","cand_A_all","cand_B_all","cand_H_all","general_sharetotal_A_all"]}

    7007 → 7007 linhas

    Frame antes → depois: 3acd08e209f7 → 72f6007995a7
16. 15Transformação
    ref_panel · filter {"query":"cycle in [2006, 2008]"}

    7007 → 803 linhas

    Frame antes → depois: 72f6007995a7 → 00c546098379
17. 16Transformação
    ref_panel · merge {"how":"inner","on":["district_final"],"with":"ref_pre"}

    803 → 795 linhas

    Frame antes → depois: 00c546098379 → 90534181e034
18. 17Transformação
    ref_panel · merge {"how":"inner","on":["district_final"],"with":"ref_post"}

    795 → 786 linhas

    Frame antes → depois: 90534181e034 → 980156996dec
19. 18Transformação
    ref_panel · filter {"query":"a_pre == 0 and (a_post == 1 or a_post == 0)"}

    786 → 772 linhas

    Frame antes → depois: 980156996dec → 3dfafd4a7664
20. 19Transformação
    ref_panel · merge {"how":"left","on":["district_final"],"with":"ref_complete"}

    772 → 772 linhas

    Frame antes → depois: 3dfafd4a7664 → ad979675077e
21. 20Transformação
    ref_panel · filter {"query":"ncycles == 2"}

    772 → 768 linhas

    Frame antes → depois: ad979675077e → 9e163d7172d0
22. 21Transformação
    ref_panel · derive {"args":{"cmp":"eq","column":"a_post","value":1},"name":"Treated","op":"indicator"}

    768 → 768 linhas

    Frame antes → depois: 9e163d7172d0 → c90cbb739051
23. 22Transformação
    ref_panel · derive {"args":{"cmp":"eq","column":"cycle","value":2008},"name":"Post","op":"indicator"}

    768 → 768 linhas

    Frame antes → depois: c90cbb739051 → 0b59cd458368
24. 23Transformação
    ref_panel · derive {"args":{"columns":["Treated","Post"]},"name":"Treated_Post","op":"interaction"}

    768 → 768 linhas

    Frame antes → depois: 0b59cd458368 → 8b9f992825d6
25. 24Turno do modelo
    call_6db1ca8creferenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

    Texto visível

    smf.ols('general_sharetotal_A_all ~ Treated + Post + Treated:Post + cand_B_all + cand_H_all').fit(cov_type='cluster', groups=district_final) (CR1 errors with statsmodels' default G/(G-1)·(N-1)/(N-K) correction)

    Chamadas pedidas

    • infer.ols

      {
        "cluster": "district_final",
        "controls": [
          "Treated",
          "Post",
          "cand_B_all",
          "cand_H_all"
        ],
        "dataset": "ref_panel",
        "x": [
          "Treated_Post"
        ],
        "y": "general_sharetotal_A_all"
      }

    Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
26. 25Ferramenta
    infer.ols— Regressão linear por mínimos quadrados ordinários (MQO)v0.1.0call_38dd916ddecidido pelo modeloreexecutável

    Motivo: smf.ols('general_sharetotal_A_all ~ Treated + Post + Treated:Post + cand_B_all + cand_H_all').fit(cov_type='cluster', groups=district_final) (CR1 errors with statsmodels' default G/(G-1)·(N-1)/(N-K) correction)

    Argumentos

    {
      "cluster": "district_final",
      "controls": [
        "Treated",
        "Post",
        "cand_B_all",
        "cand_H_all"
      ],
      "dataset": "ref_panel",
      "fe": [],
      "intercept": true,
      "level": 0.95,
      "se": "HC1",
      "subset": null,
      "weights": null,
      "x": [
        "Treated_Post"
      ],
      "y": "general_sharetotal_A_all"
    }

    Figuras call_38dd916d

    Chave	Valor	Valor bruto	Erro-padrão	IC
    coef.Treated_Post Coefficient on Treated_Post in the regression of general_sharetotal_A_all	0,0734181	0.0734181152562507	0,04637750.04637745183950981	-0,0177682 – 0,164604

    Diagnósticos

    • n_obs = 768768 (observations used)
    • k = 66 (parameters estimated, fixed-effect dummies included)
    • r2 = 0,07941470.0794147497797385
    • df_resid = 762762
    • se_type = "cluster" (cluster-robust standard errors (CR1) by 'district_final')
    • n_clusters = 384384 (384 clusters of 'district_final')

    Derivação matemática e pressupostos

    coef.Treated_Post

    1. Coeficiente de Treated_Post na regressão por mínimos quadrados ordinários de general_sharetotal_A_all.→ 0,0734181 (0.0734181152562507)
    2. Erro-padrão robusto por conglomerado (CR1): a raiz da variância estimada do coeficiente.ep = √V_jj→ 0,0463775 (0.04637745183950981)
    3. Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 383 graus de liberdade vezes o erro-padrão.IC = β ± c · ep

    Pressupostos do estimador

    • the conditional mean of the outcome is linear in the regressors
    • errors are independent across clusters of 'district_final' and may be correlated within them

    Tabelas (1)

    {
      "coefficients": [
        {
          "ci_high": 0.02994659675855081,
          "ci_low": 0.018201479153710808,
          "coef": 0.02407403795613081,
          "p": 9.817360029225654e-15,
          "se": 0.002986790218533347,
          "t": 8.060170348338788,
          "term": "Intercept"
        },
        {
          "ci_high": 0.16460440324659428,
          "ci_low": -0.017768172734092896,
          "coef": 0.0734181152562507,
          "p": 0.11423400772368443,
          "se": 0.04637745183950981,
          "t": 1.5830562556631118,
          "term": "Treated_Post"
        },
        {
          "ci_high": 0.12638815964169361,
          "ci_low": -0.0074821885911809174,
          "coef": 0.059452985525256345,
          "p": 0.08154442190401828,
          "se": 0.034043307194202364,
          "t": 1.7463927692483794,
          "term": "Treated"
        },
        {
          "ci_high": -0.0003917172530841879,
          "ci_low": -0.011687923709795666,
          "coef": -0.0060398204814399266,
          "p": 0.036158289745232886,
          "se": 0.002872631853216653,
          "t": -2.102538992136005,
          "term": "Post"
        },
        {
          "ci_high": 0.04505362505575269,
          "ci_low": 0.0036370924244222697,
          "coef": 0.02434535874008748,
          "p": 0.02133496691331671,
          "se": 0.010532248267812044,
          "t": 2.311506348980601,
          "term": "cand_B_all"
        },
        {
          "ci_high": 0.03399484546327532,
          "ci_low": -0.0024194859076863973,
          "coef": 0.01578767977779446,
          "p": 0.08902419977541053,
          "se": 0.009260185586255954,
          "t": 1.7048988522677848,
          "term": "cand_H_all"
        }
      ]
    }

    Hashes SHA-256 e metadados de reprodução

    Digests dos dados

    ref_panel 8b9f992825d68a5218ea0d5964840afceed0e9c1a991784a195e7105081b6e5e

    sha256 do resultado

    4199e83ec77bed55b80e34d5cebccb5e4f9ffc8728961600fab683abd75fcd1e

    Resultado mostrado ao modelo
27. 26Turno do modelo
    call_b760f838referenceFim: 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.Treated_Post = 0,07342. Reference path transcribed from the item's own estimation script.",
        "caveats": [],
        "figure_refs": [
          "call_38dd916d:coef.Treated_Post"
        ]
      }

    Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
28. 27Guarda
    resposta aceita

    Números encontrados

    0,07342

    Sem lastro

    —

    Não citados

    —

    1 nova(s) tentativa(s) restante(s)
29. 28Resposta

    Estimativa (infer.ols): coef.Treated_Post = 0,07342. Reference path transcribed from the item's own estimation script.

    Figuras citadas: call_38dd916d:coef.Treated_Post · Parada: answered

    Figura da resposta: call_38dd916d:coef.Treated_Post = 0,07341810.0734181152562507 (se 0,04637750.04637745183950981)
30. 29Pontuação
    #0resposta com lastropassouobrigatóriaevery number backed
31. 30Pontuação
    #1figura da resposta próximapassouobrigatória0.07341811526 in [-0.01748169035, 0.1643179209]?
32. 31Pontuação
    #2figura da resposta próximapassouregistrada0.07341811526 vs 0.07341811525625276
33. 32Pontuação
    #3figura da resposta próximapassouregistrada0.04637745184 vs 0.04637745183951293

Épocas × verificações

Tentativas

Cada tentativa é seu próprio arquivo e é publicada; só a última de cada época é pontuada.

• época 1, tentativa 1passoupontuada(exibindo)

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