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Tarefa crb_papers.q003

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

How much did state policy liberalism change, by the governor's first year in office, in states that elected a Democratic governor in 1971 relative to comparable states that remained under Republican governors?

Resposta

Estimativa ingênua (infer.ols): coef.gov_dem = -0,1543.

Valores esperados e de onde vêm

crb_papers.q003.gold

Fonte
CausalReasoningBenchmark, causal_queries.json, index 3 (revision edd0fe1c1d1c)
Tipo
benchmark_gold
Localização
causal_queries.json['3'].effect and .standard_error
Trecho
index 3: effect 0.0372603, standard_error 0.103927
Valor como publicado
0.037260278134351676
Erro-padrão como publicado
0.1039269262785647
Valor
0,03726030.037260278134351676 (se 0,1039270.1039269262785647)
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: 0,03726030.037260278134351676 · relativa 0.000001 · absoluta 1.039269262785647e-7
  • #3 figura da resposta próxima: 0,1039270.1039269262785647 · relativa 0.000001 · absoluta 1.039269262785647e-7

crb_papers.q003.script

Fonte
CausalReasoningBenchmark reference estimation script for index 3
Tipo
software
Localização
solutions/research_papers/DiD/Caughey_etal_2017/question_1/estimation.py
Valor
—
Conferido por
arquivo do benchmark · sha256 of the script's 2129 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
14c0081a51dcc6aebf93229ac402590aa8a057d600ff3a036b8fce094e7a5244 (identifica a cópia lida, não um arquivo canônico)
Endereço
https://huggingface.co/datasets/syrgkanislab/CausalReasoningBenchmark/resolve/edd0fe1c1d1c5c0a4f0e3e67a50481ac679fb888/solutions/research_papers/DiD/Caughey_etal_2017/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
    -0.1543024516 in [-0.1664364974, 0.2409570536]?
    obrigatóriapassou[-0.16643649737163513,0.24095705364033848]-0,154302-0.1543024516209968interval · [-0.16643649737163513, 0.24095705364033848]—call_2c13f067
    2figura da resposta próxima
    -0.1543024516 vs 0.037260278134351676
    registradafalhou0,03726030.037260278134351676-0,154302-0.1543024516209968relativa 0.000001 · absoluta 1.039269262785647e-7crb_papers.q003.goldcall_2c13f067
    3figura da resposta próxima
    0.03100775329 vs 0.1039269262785647
    registradafalhou0,1039270.10392692627856470,03100780.031007753286602834relativa 0.000001 · absoluta 1.039269262785647e-7crb_papers.q003.goldcall_2c13f067

    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_2c13f067)igual
    Argumentos da referência e do agente
    ArgumentoReferênciaAgente
    cluster"state"—diferente
    controls["TreatGroup","Post","Policy_L1","Policy_L2"][]diferente
    dataset"ref_panel""ds_0002"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["TreatGroup_Post"]["gov_dem"]diferente
    y"Policy""Policy"igual

    Trajetória

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

    1. 0Início

      How much did state policy liberalism change, by the governor's first year in office, in states that elected a Democratic governor in 1971 relative to comparable states that remained under Republican governors?

      ds_0002 (agent)

      | Variable Name | Description | | --- | --- | | year | Calendar year of the observation (1936–2014). Each row represents a state-year in the panel. | | Policy | Continuous, standardized index summarizing the ideological orientation of a state’s policy outputs in that year. The score is estimated from nearly 150 statutory and regulatory indicators spanning domains such as social welfare, taxation, labor, civil rights, women’s rights, morality laws, family planning, environment, religion, criminal justice, and drug policy. Higher values indicate a more liberal policy orientation. The scale is standardized to be comparable across states and years (centered roughly at 0 with a typical standard deviation near 1). | | sen_dem_control | Indicator (0/1) that the Democratic Party held a majority of seats in the state senate (upper chamber) in that year, based on official election returns and chamber composition. For Nebraska’s nonpartisan unicameral legislature, this variable is generally coded as missing. | | hs_dem_control | Indicator (0/1) that the Democratic Party held a majority of seats in the state house/lower chamber in that year, based on official election returns and chamber composition. For Nebraska, typically coded as missing due to nonpartisan unicameral structure. | | Policy_L1 | One-year lag of the policy index (value from t−1). Included to capture persistence and gradualism in state policy change over time. | | Policy_L2 | Two-year lag of the policy index (value from t−2). Provides an additional look-back to characterize medium-run policy momentum. | | South11 | Indicator (0/1) for membership in the traditional 11-state U.S. South: AL, AR, FL, GA, LA, MS, NC, SC, TN, TX, VA. Useful for distinguishing long-standing regional political and institutional contexts. | | gov_dem | Indicator (0/1) that the sitting governor was affiliated with the Democratic Party for the relevant portion of the calendar year, as compiled from official administrative records and election data. | | gov_dem_X_hs_dem | Product term (0/1) equal to 1 when both gov_dem = 1 and hs_dem_control = 1 in the same year; 0 otherwise. Encodes concurrent Democratic control of the governorship and the lower chamber. | | gov_dem_X_sen_dem | Product term (0/1) equal to 1 when both gov_dem = 1 and sen_dem_control = 1 in the same year; 0 otherwise. Encodes concurrent Democratic control of the governorship and the upper chamber. | | both_dem | Indicator (0/1) that Democrats controlled both legislative chambers in that year (hs_dem_control = 1 and sen_dem_control = 1). | | full_dem | Indicator (0/1) that Democrats held a “trifecta” in that year: governorship plus majorities in both legislative chambers. | | state | Numeric state identifier for the panel unit. Codes map to U.S. states included in the panel; Nebraska’s unicameral, nonpartisan legislature is handled specially for chamber-control variables. | Dataset Context: This dataset is an annual, state-year panel that tracks the ideological orientation of U.S. state policies alongside the party composition of key state institutions from the mid-1930s through the early 2010s. The central measure is a dynamic policy index that pools information from nearly 150 codings of enacted state statutes and regulatory provisions. The underlying source material includes historical statutory compilations, administrative records, and archival policy datasets, covering areas such as safety-net programs, taxation structures, labor and employment regulations, civil and voting rights, women’s rights, morality and family policy, environmental regulation, religion in public institutions, criminal justice, and drug laws. Because most policy indicators are dichotomous (present/absent) and some are continuous (e.g., benefit levels), the index is estimated using a modeling approach that accommodates mixed data types and allows comparability over time. Scores are standardized so that higher values represent more liberal policy bundles, facilitating interpretation across decades when the national policy environment shifted. Party control variables are constructed from state election returns and official chamber rosters, identifying which party held the governor’s office and whether Democrats held majorities in the state house and senate in each year. Nebraska’s unique nonpartisan unicameral legislature is typically excluded from chamber-majority coding or marked as missing for those fields. The dataset also includes composite indicators for concurrent control across branches (e.g., both chambers, or governorship plus both chambers) and simple product terms that register simultaneous control by party across institutions. Coverage spans 1936–2014, a period that includes major institutional realignments, changes in party competition, and significant waves of policy innovation at the state level. The inclusion of lagged versions of the policy index reflects the empirical regularity that state policy changes are often incremental and path dependent. A regional flag for the traditional 11-state South is provided to facilitate analyses that account for historically distinct political and legal environments in that region. Together, these features make the file a versatile panel for describing long-run patterns in state policy content and the composition of state political institutions.

      file cde09ab5c48545fe73a7e45bba4331903448516e4a49ebe1e1c8b00723f91b7e · frame —
      ref_g0 (reference)
      file cde09ab5c48545fe73a7e45bba4331903448516e4a49ebe1e1c8b00723f91b7e · frame —
      1. select {"columns":["year","state","Policy","gov_dem","Policy_L1","Policy_L2"]}
      2. derive {"args":{"cmp":"gt","column":"gov_dem","value":0.5},"name":"gov_dem","op":"indicator"}
      3. derive {"args":{"column":"gov_dem","default":0,"keep_unmapped":false,"mapping":[{"source":1,"target":1}]},"name":"gov_dem","op":"map"}
      4. dropna {"columns":["year","state","Policy","gov_dem"]}
      5. filter {"query":"year == 1970"}
      6. select {"columns":["state","gov_dem"]}
      7. rename {"map":{"gov_dem":"g0"}}
      ref_g1 (reference)
      file cde09ab5c48545fe73a7e45bba4331903448516e4a49ebe1e1c8b00723f91b7e · frame —
      1. select {"columns":["year","state","Policy","gov_dem","Policy_L1","Policy_L2"]}
      2. derive {"args":{"cmp":"gt","column":"gov_dem","value":0.5},"name":"gov_dem","op":"indicator"}
      3. derive {"args":{"column":"gov_dem","default":0,"keep_unmapped":false,"mapping":[{"source":1,"target":1}]},"name":"gov_dem","op":"map"}
      4. dropna {"columns":["year","state","Policy","gov_dem"]}
      5. filter {"query":"year == 1971"}
      6. select {"columns":["state","gov_dem"]}
      7. rename {"map":{"gov_dem":"g1"}}
      ref_panel (reference)
      file cde09ab5c48545fe73a7e45bba4331903448516e4a49ebe1e1c8b00723f91b7e · frame —
      1. select {"columns":["year","state","Policy","gov_dem","Policy_L1","Policy_L2"]}
      2. derive {"args":{"cmp":"gt","column":"gov_dem","value":0.5},"name":"gov_dem","op":"indicator"}
      3. derive {"args":{"column":"gov_dem","default":0,"keep_unmapped":false,"mapping":[{"source":1,"target":1}]},"name":"gov_dem","op":"map"}
      4. dropna {"columns":["year","state","Policy","gov_dem"]}
      5. filter {"query":"year in [1970, 1971]"}
      6. merge {"how":"inner","on":["state"],"with":"ref_g0"}
      7. merge {"how":"inner","on":["state"],"with":"ref_g1"}
      8. filter {"query":"g0 == 0 and (g1 == 1 or g1 == 0)"}
      9. derive {"args":{"cmp":"eq","column":"year","value":1971},"name":"Post","op":"indicator"}
      10. derive {"args":{"cmp":"eq","column":"g1","value":1},"name":"TreatGroup","op":"indicator"}
      11. derive {"args":{"columns":["TreatGroup","Post"]},"name":"TreatGroup_Post","op":"interaction"}
    2. 1Turno do modelo
      call_d16b49d4naive-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_0002",
          "x": [
            "gov_dem"
          ],
          "y": "Policy"
        }
    3. 2Ferramenta
      infer.olsv0.1.0call_2c13f067decidido pelo modeloreexecutável

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

      Argumentos
      {
        "cluster": null,
        "controls": [],
        "dataset": "ds_0002",
        "fe": [],
        "intercept": true,
        "level": 0.95,
        "se": "HC1",
        "subset": null,
        "weights": null,
        "x": [
          "gov_dem"
        ],
        "y": "Policy"
      }
      Figuras call_2c13f067
      ChaveValorValor brutoErro-padrãoIC
      coef.gov_dem
      Coefficient on gov_dem in the regression of Policy
      -0,154302 -0.15430245162099680,03100780.031007753286602834-0,215095 – -0,0935095
      Diagnósticos
      • n_obs = 3.9043904
      • k = 22
      • r2 = 0,006280170.006280167993056307
      • df_resid = 3.9023902
      • se_type = "HC1"
      • n_clusters = —
      Tabelas (1)
      {
        "coefficients": [
          {
            "ci_high": 0.13312915747750606,
            "ci_low": 0.04341078306431639,
            "coef": 0.08826997027091123,
            "p": 0.00011623977565694068,
            "se": 0.022880661443021613,
            "t": 3.8578417188998153,
            "term": "Intercept"
          },
          {
            "ci_high": -0.09350951462468195,
            "ci_low": -0.21509538861731164,
            "coef": -0.1543024516209968,
            "p": 6.762339798583586e-7,
            "se": 0.031007753286602834,
            "t": -4.976253848345229,
            "term": "gov_dem"
          }
        ]
      }
      • 46 rows were dropped because one of 'Policy', 'gov_dem' was missing
      Digests dos dados
      ds_0002 3dd8fb457073ac61d9d41273bd6c182d894366d9601588c9a90bd54929af231f
      sha256 do resultado
      d8c9a0866743e93845f74465de06be69c70908b892b171244b20a2ab443c9ce6
    4. 3Turno do modelo
      call_8bca8e5enaive-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.gov_dem = -0,1543.",
          "caveats": [],
          "figure_refs": [
            "call_2c13f067:coef.gov_dem"
          ]
        }
    5. 4Guarda
      resposta aceita
      Números encontrados
      -0,1543
      Sem lastro
      —
      Não citados
      —
      1 nova(s) tentativa(s) restante(s)
    6. 5Resposta

      Estimativa ingênua (infer.ols): coef.gov_dem = -0,1543.

      Figuras citadas: call_2c13f067:coef.gov_dem · Parada: answered
      Figura da resposta: call_2c13f067:coef.gov_dem = -0,154302-0.1543024516209968 (se 0,03100780.031007753286602834)
    7. 6Pontuação
      #0resposta com lastropassouobrigatóriaevery number backed
    8. 7Pontuação
      #1figura da resposta próximapassouobrigatória-0.1543024516 in [-0.1664364974, 0.2409570536]?
    9. 8Pontuação
      #2figura da resposta próximafalhouregistrada-0.1543024516 vs 0.037260278134351676
    10. 9Pontuação
      #3figura da resposta próximafalhouregistrada0.03100775329 vs 0.1039269262785647

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    Época 1passoupassoufalhoufalhou

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