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Tarefa crb_papers.q023

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

Veredito

falhouDecidida pela verificação 1: figura da resposta próxima, fora da tolerância.
-0.8102391935 in [-0.2495215109, 0.06779465595]?

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

Pergunta feita ao analista

By how much did the State Democracy Index change from 2002 to 2004 in states that newly gained unified Republican control in 2003 versus states that did not?

Resposta

Estimativa ingênua (infer.ols): coef.Republican = -0,8102.

Valores esperados e de onde vêm

crb_papers.q023.gold

Fonte
CausalReasoningBenchmark, causal_queries.json, index 23 (revision edd0fe1c1d1c)
Tipo
benchmark_gold
Localização
causal_queries.json['23'].effect and .standard_error
Trecho
index 23: effect -0.0908634, standard_error 0.080948
Valor como publicado
-0.09086342748982335
Erro-padrão como publicado
0.08094800175309143
Valor
-0,0908634-0.09086342748982335 (se 0,0809480.08094800175309143)
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: -0,0908634-0.09086342748982335 · relativa 0.000001 · absoluta 8.094800175309143e-8
  • #3 figura da resposta próxima: 0,0809480.08094800175309143 · relativa 0.000001 · absoluta 8.094800175309143e-8

crb_papers.q023.script

Fonte
CausalReasoningBenchmark reference estimation script for index 23
Tipo
software
Localização
solutions/research_papers/DiD/Grumbach_2023/question_2/estimation.py
Valor
—
Conferido por
arquivo do benchmark · sha256 of the script's 3253 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
0588cb491837cd73bf22e1c4d5cd874a02113d9b681d69c73fa44caa5d692eb2 (identifica a cópia lida, não um arquivo canônico)
Endereço
https://huggingface.co/datasets/syrgkanislab/CausalReasoningBenchmark/resolve/edd0fe1c1d1c5c0a4f0e3e67a50481ac679fb888/solutions/research_papers/DiD/Grumbach_2023/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
    -0.8102391935 in [-0.2495215109, 0.06779465595]?
    obrigatóriafalhou[-0.24952151092588254,0.06779465594623585]-0,810239-0.8102391935440542interval · [-0.24952151092588254, 0.06779465594623585]—call_9bed1b3d
    2figura da resposta próxima
    -0.8102391935 vs -0.09086342748982335
    registradafalhou-0,0908634-0.09086342748982335-0,810239-0.8102391935440542relativa 0.000001 · absoluta 8.094800175309143e-8crb_papers.q023.goldcall_9bed1b3d
    3figura da resposta próxima
    0.07061197387 vs 0.08094800175309143
    registradafalhou0,0809480.080948001753091430,0706120.07061197387291988relativa 0.000001 · absoluta 8.094800175309143e-8crb_papers.q023.goldcall_9bed1b3d

    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_9bed1b3d)igual
    Argumentos da referência e do agente
    ArgumentoReferênciaAgente
    cluster"state"—diferente
    controls["Tgroup","Post"][]diferente
    dataset"ref_panel""ds_0012"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["DID"]["Republican"]diferente
    y"democracy_mcmc""democracy_mcmc"igual

    Trajetória

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

    1. 0Início

      By how much did the State Democracy Index change from 2002 to 2004 in states that newly gained unified Republican control in 2003 versus states that did not?

      ds_0012 (agent)

      | Variable Name | Description | | --- | --- | | index | Sequential row identifier from the file export; included for reference and not used as a substantive measure. | | Republican | Binary indicator (0/1) for unified Republican control of state government in a given year. Equals 1 when the governorship and both legislative chambers were held by Republicans; 0 otherwise (including divided or other control). Compiled from state partisan balance data with updates from legislative tracking organizations. | | state | U.S. state name (50 states). | | year | Calendar year of observation (2000–2016). | | democracy_mcmc | Continuous State Democracy Index score for the state-year, estimated via Bayesian factor analysis (MCMC) from 51 state-level indicators of electoral rules, administrative performance, districting metrics, and related practices. The scale is approximately standardized across the panel (values can be negative or positive around zero); higher values indicate stronger electoral-democracy performance. Sources feeding the indicators include election administration data (e.g., MIT Election Lab), legislative policy compilations (e.g., NCSL), gerrymandering diagnostics, and measures of policy–opinion congruence. | Dataset Context: This file is a state–year panel that assembles administrative records, policy codings, and quantitative diagnostics to summarize how U.S. states structure and administer electoral democracy. The period 2000–2016 spans consequential developments in state election law and administration, including reforms that expanded access (e.g., early voting, online and same‑day registration, and the beginnings of automatic voter registration late in the period) and policies that increased participation costs for some voters (e.g., voter ID requirements). It also covers the 2010 redistricting cycle and subsequent evaluations of district plans using established gerrymandering metrics. During these years, federal–state relations affecting elections shifted as well, including changes to Voting Rights Act oversight after 2013, which altered how and where states implemented election-law changes. The State Democracy Index (reported here as democracy_mcmc) is constructed by combining 51 indicators into a single latent score using Bayesian factor analysis designed for mixed data types (binary, ordinal, and continuous). Indicator families include: election access rules (registration and voting modalities), administrative performance (e.g., wait times, provisional ballot handling, data completeness), post‑election audit requirements, districting fairness diagnostics (efficiency gap, partisan symmetry, declination, compactness), and measures of policy–opinion alignment. Indicator inputs draw on sources such as the MIT Election Lab’s compilations of election administration statistics, the National Conference of State Legislatures’ inventories of state election laws, quantitative assessments of district plans, and state policy–opinion datasets. Scores are standardized to facilitate comparison across states and years, with higher values reflecting stronger performance on these electoral-democracy dimensions. The unified-party-control variable is built from long‑running state partisan balance datasets and contemporary updates that track which party simultaneously held the governorship and majorities in both legislative chambers in each year. Together with state names and calendar years, these fields allow analysts to align institutional configurations with contemporaneous features of election law, administration, and districting captured in the index.

      file c41bdacd62be74293e7a28a6c5d82a460ccf5f046e080a7443363bc59ff73fb0 · frame —
      ref_panel (reference)
      file c41bdacd62be74293e7a28a6c5d82a460ccf5f046e080a7443363bc59ff73fb0 · frame —
      1. select {"columns":["Republican","state","year","democracy_mcmc"]}
      2. dropna {"columns":["state","year","Republican","democracy_mcmc"]}
      3. filter {"query":"year in [2002, 2004]"}
      4. merge {"how":"left","on":["state"],"with":"ref_switch"}
      5. merge {"how":"inner","on":["state"],"with":"ref_pre"}
      6. merge {"how":"inner","on":["state"],"with":"ref_post"}
      7. filter {"query":"r_pre == 0 and ((syear == 2003 and r_post == 1) or r_post == 0)"}
      8. derive {"args":{"cmp":"eq","column":"r_post","value":1},"name":"Tgroup","op":"indicator"}
      9. derive {"args":{"cmp":"eq","column":"year","value":2004},"name":"Post","op":"indicator"}
      10. derive {"args":{"columns":["Tgroup","Post"]},"name":"DID","op":"interaction"}
      ref_post (reference)
      file c41bdacd62be74293e7a28a6c5d82a460ccf5f046e080a7443363bc59ff73fb0 · frame —
      1. select {"columns":["Republican","state","year","democracy_mcmc"]}
      2. dropna {"columns":["state","year","Republican","democracy_mcmc"]}
      3. filter {"query":"year == 2004"}
      4. select {"columns":["state","Republican"]}
      5. rename {"map":{"Republican":"r_post"}}
      ref_pre (reference)
      file c41bdacd62be74293e7a28a6c5d82a460ccf5f046e080a7443363bc59ff73fb0 · frame —
      1. select {"columns":["Republican","state","year","democracy_mcmc"]}
      2. dropna {"columns":["state","year","Republican","democracy_mcmc"]}
      3. filter {"query":"year == 2002"}
      4. select {"columns":["state","Republican"]}
      5. rename {"map":{"Republican":"r_pre"}}
      ref_switch (reference)
      file c41bdacd62be74293e7a28a6c5d82a460ccf5f046e080a7443363bc59ff73fb0 · frame —
      1. select {"columns":["Republican","state","year","democracy_mcmc"]}
      2. dropna {"columns":["state","year","Republican","democracy_mcmc"]}
      3. derive {"args":{"by":["state"],"column":"Republican","order":["year"],"periods":1},"name":"r_prev","op":"lag"}
      4. filter {"query":"Republican == 1 and r_prev == 0"}
      5. collapse {"aggs":[{"column":"year","fn":"min","name":"syear"}],"by":["state"]}
    2. 1Turno do modelo
      call_c3f33040naive-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_0012",
          "x": [
            "Republican"
          ],
          "y": "democracy_mcmc"
        }
    3. 2Ferramenta
      infer.olsv0.1.0call_9bed1b3ddecidido pelo modeloreexecutável

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

      Argumentos
      {
        "cluster": null,
        "controls": [],
        "dataset": "ds_0012",
        "fe": [],
        "intercept": true,
        "level": 0.95,
        "se": "HC1",
        "subset": null,
        "weights": null,
        "x": [
          "Republican"
        ],
        "y": "democracy_mcmc"
      }
      Figuras call_9bed1b3d
      ChaveValorValor brutoErro-padrãoIC
      coef.Republican
      Coefficient on Republican in the regression of democracy_mcmc
      -0,810239 -0.81023919354405420,0706120.07061197387291988-0,948838 – -0,67164
      Diagnósticos
      • n_obs = 833833
      • k = 22
      • r2 = 0,1694770.16947739786251625
      • df_resid = 831831
      • se_type = "HC1"
      • n_clusters = —
      Tabelas (1)
      {
        "coefficients": [
          {
            "ci_high": 0.32846289276375057,
            "ci_low": 0.20951703481464037,
            "coef": 0.2689899637891955,
            "p": 4.1451826913649454e-18,
            "se": 0.03029969344550972,
            "t": 8.877646378599207,
            "term": "Intercept"
          },
          {
            "ci_high": -0.6716404016305455,
            "ci_low": -0.9488379854575628,
            "coef": -0.8102391935440542,
            "p": 2.1433815010101494e-28,
            "se": 0.07061197387291988,
            "t": -11.47452973064085,
            "term": "Republican"
          }
        ]
      }
      Digests dos dados
      ds_0012 5bc3cfb33d7015ef69f405f7a413176b19165dd8a84304cbc15597025a3680f8
      sha256 do resultado
      93a1214eb95f5078ca136db5d9737674a869f92c8b5e1c14094129faf17b7aa7
    4. 3Turno do modelo
      call_642bcb63naive-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.Republican = -0,8102.",
          "caveats": [],
          "figure_refs": [
            "call_9bed1b3d:coef.Republican"
          ]
        }
    5. 4Guarda
      resposta aceita
      Números encontrados
      -0,8102
      Sem lastro
      —
      Não citados
      —
      1 nova(s) tentativa(s) restante(s)
    6. 5Resposta

      Estimativa ingênua (infer.ols): coef.Republican = -0,8102.

      Figuras citadas: call_9bed1b3d:coef.Republican · Parada: answered
      Figura da resposta: call_9bed1b3d:coef.Republican = -0,810239-0.8102391935440542 (se 0,0706120.07061197387291988)
    7. 6Pontuação
      #0resposta com lastropassouobrigatóriaevery number backed
    8. 7Pontuação
      #1figura da resposta próximafalhouobrigatória-0.8102391935 in [-0.2495215109, 0.06779465595]?
    9. 8Pontuação
      #2figura da resposta próximafalhouregistrada-0.8102391935 vs -0.09086342748982335
    10. 9Pontuação
      #3figura da resposta próximafalhouregistrada0.07061197387 vs 0.08094800175309143

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

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