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Tarefa crb_papers.q015

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

Veredito

falhouDecidida pela verificação 1: figura da resposta próxima, fora da tolerância.
0.03628325247 in [-0.03230748536, -0.01363708281]?

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

Pergunta feita ao analista

Did cities that adopted CompStat in 1996 experience a change in the share of total arrests that were minor (Part II) offenses from 1995 to 1997 relative to cities that had not yet adopted (1998+)?

Resposta

Estimativa ingênua (infer.ols): coef.HASCOMPSTAT = 0,03628.

Valores esperados e de onde vêm

crb_papers.q015.gold

Fonte
CausalReasoningBenchmark, causal_queries.json, index 15 (revision edd0fe1c1d1c)
Tipo
benchmark_gold
Localização
causal_queries.json['15'].effect and .standard_error
Trecho
index 15: effect -0.0229723, standard_error 0.00476286
Valor como publicado
-0.02297228408669323
Erro-padrão como publicado
0.004762857794788859
Valor
-0,0229723-0.02297228408669323 (se 0,004762860.004762857794788859)
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: -0,0229723-0.02297228408669323 · relativa 0.000001 · absoluta 4.762857794788859e-9
  • #3 figura da resposta próxima: 0,004762860.004762857794788859 · relativa 0.000001 · absoluta 4.762857794788859e-9

crb_papers.q015.script

Fonte
CausalReasoningBenchmark reference estimation script for index 15
Tipo
software
Localização
solutions/research_papers/DiD/Eckhouse_2022/question_2/estimation.py
Valor
—
Conferido por
arquivo do benchmark · sha256 of the script's 1837 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
271e7f5317526ecee3f6ee4bd01d557ac116b8aef3b94f1243e0a9bbad3d6ec3 (identifica a cópia lida, não um arquivo canônico)
Endereço
https://huggingface.co/datasets/syrgkanislab/CausalReasoningBenchmark/resolve/edd0fe1c1d1c5c0a4f0e3e67a50481ac679fb888/solutions/research_papers/DiD/Eckhouse_2022/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
    0.03628325247 in [-0.03230748536, -0.01363708281]?
    obrigatóriafalhou[-0.032307485364479395,-0.013637082808907068]0,03628330.0362832524726211interval · [-0.032307485364479395, -0.013637082808907068]—call_c4d6ac40
    2figura da resposta próxima
    0.03628325247 vs -0.02297228408669323
    registradafalhou-0,0229723-0.022972284086693230,03628330.0362832524726211relativa 0.000001 · absoluta 4.762857794788859e-9crb_papers.q015.goldcall_c4d6ac40
    3figura da resposta próxima
    0.004028659853 vs 0.004762857794788859
    registradafalhou0,004762860.0047628577947888590,004028660.0040286598526058275relativa 0.000001 · absoluta 4.762857794788859e-9crb_papers.q015.goldcall_c4d6ac40

    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_c4d6ac40)igual
    Argumentos da referência e do agente
    ArgumentoReferênciaAgente
    cluster"AGENCY"—diferente
    controls["Treat","Post"][]diferente
    dataset"ref_panel""ds_0008"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["Treat_Post"]["HASCOMPSTAT"]diferente
    y"SHAREPT2""SHAREPT2"igual

    Trajetória

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

    1. 0Início

      Did cities that adopted CompStat in 1996 experience a change in the share of total arrests that were minor (Part II) offenses from 1995 to 1997 relative to cities that had not yet adopted (1998+)?

      ds_0008 (agent)

      | Variable Name | Description | | --- | --- | | SHAREPT2 | Proportion (0–1) of a department’s total arrests in a given calendar year that fall under Uniform Crime Reporting (UCR) Part II offense categories. Part II includes drug abuse violations, simple assault, fraud/forgery, stolen property, vandalism, weapons violations, prostitution, disorderly conduct, and other non‑index offenses. Computed as Part II arrests divided by all arrests reported for that year. | | HASCOMPSTAT | Binary indicator (0/1) that the police department operated a CompStat‑style performance/metrics management system in that year. “CompStat‑style” covers systems that regularly compile incident/arrest statistics, visualize patterns, and use management meetings for accountability once implemented and in subsequent years. | | AGENCY | Text identifier for the municipal police department/jurisdiction (e.g., “PHOENIX”). Serves as the unit index across years and corresponds to the primary city police agency reporting to the UCR program. | | YEAR | Calendar year of observation, aligning with UCR annual reporting (1990–2013). | | PART2arrests | Annual count of arrests recorded for UCR Part II offenses by the department. Includes adult and juvenile arrests as submitted to the UCR program. Integer count. | | POP | Resident population of the city for that year, based on UCR population figures (census-based estimates used by UCR for rate calculations). | Dataset Context: This file is a municipal police department–by–year panel for large U.S. cities covering 1990–2013. It merges administrative statistics from the FBI’s Uniform Crime Reporting (UCR) program with hand-collected information on whether and when departments used CompStat‑style metrics management. UCR provides standardized definitions, monthly submissions from local agencies, and annual tabulations of arrests and population. Part II arrests span a wide set of non‑index offenses—such as drug violations, simple assault, property and public‑order offenses—that are often more discretionary than index (Part I) crimes. The population series comes from UCR’s census‑based annual estimates and is commonly used to construct per‑capita measures. CompStat‑style systems began spreading among U.S. police departments in the mid‑1990s alongside advances in data systems and the broader rise of performance management in public administration. These systems typically feature frequent data reviews, mapping and trend visualization, and management meetings focused on recent activity. Adoption timing in this dataset was compiled from public sources such as department websites, press releases, and news archives, and is coded as “on” starting in the first documented year of operation. Each row represents a department-year and includes: the department identifier, the year, the count of arrests for UCR Part II offenses, the share of all arrests that are Part II, whether a CompStat‑style system was in use, and the city’s population. Because arrests are administrative measures of police activity and recording practices, they are not the same as offenses known to police or victimization; nonetheless, UCR’s standardized categories enable comparisons across cities and over time. The panel captures a period of substantial organizational change in U.S. municipal policing, during which many large departments modernized data systems and management processes while continuously reporting arrest totals to the UCR program.

      file 44d92e04bfa13eb2ab33b50c943cce8cda89582240c42d68917d74656dd90212 · frame —
      ref_adopt (reference)
      file 44d92e04bfa13eb2ab33b50c943cce8cda89582240c42d68917d74656dd90212 · frame —
      1. select {"columns":["SHAREPT2","HASCOMPSTAT","AGENCY","YEAR"]}
      2. dropna {"columns":["YEAR","HASCOMPSTAT","SHAREPT2","AGENCY"]}
      3. filter {"query":"HASCOMPSTAT == 1"}
      4. collapse {"aggs":[{"column":"YEAR","fn":"min","name":"adopt"}],"by":["AGENCY"]}
      ref_panel (reference)
      file 44d92e04bfa13eb2ab33b50c943cce8cda89582240c42d68917d74656dd90212 · frame —
      1. select {"columns":["SHAREPT2","HASCOMPSTAT","AGENCY","YEAR"]}
      2. dropna {"columns":["YEAR","HASCOMPSTAT","SHAREPT2","AGENCY"]}
      3. filter {"query":"YEAR in [1995, 1997]"}
      4. merge {"how":"inner","on":["AGENCY"],"with":"ref_adopt"}
      5. merge {"how":"left","on":["AGENCY"],"with":"ref_years"}
      6. filter {"query":"(adopt == 1996 or adopt >= 1998) and nyears == 2"}
      7. derive {"args":{"cmp":"eq","column":"YEAR","value":1997},"name":"Post","op":"indicator"}
      8. derive {"args":{"cmp":"eq","column":"adopt","value":1996},"name":"Treat","op":"indicator"}
      9. derive {"args":{"columns":["Treat","Post"]},"name":"Treat_Post","op":"interaction"}
      ref_years (reference)
      file 44d92e04bfa13eb2ab33b50c943cce8cda89582240c42d68917d74656dd90212 · frame —
      1. select {"columns":["SHAREPT2","HASCOMPSTAT","AGENCY","YEAR"]}
      2. dropna {"columns":["YEAR","HASCOMPSTAT","SHAREPT2","AGENCY"]}
      3. filter {"query":"YEAR in [1995, 1997]"}
      4. collapse {"aggs":[{"column":"SHAREPT2","fn":"count","name":"n"}],"by":["AGENCY","YEAR"]}
      5. collapse {"aggs":[{"column":"n","fn":"count","name":"nyears"}],"by":["AGENCY"]}
    2. 1Turno do modelo
      call_30831f31naive-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_0008",
          "x": [
            "HASCOMPSTAT"
          ],
          "y": "SHAREPT2"
        }
    3. 2Ferramenta
      infer.olsv0.1.0call_c4d6ac40decidido pelo modeloreexecutável

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

      Argumentos
      {
        "cluster": null,
        "controls": [],
        "dataset": "ds_0008",
        "fe": [],
        "intercept": true,
        "level": 0.95,
        "se": "HC1",
        "subset": null,
        "weights": null,
        "x": [
          "HASCOMPSTAT"
        ],
        "y": "SHAREPT2"
      }
      Figuras call_c4d6ac40
      ChaveValorValor brutoErro-padrãoIC
      coef.HASCOMPSTAT
      Coefficient on HASCOMPSTAT in the regression of SHAREPT2
      0,0362833 0.03628325247262110,004028660.00402865985260582750,0283779 – 0,0441887
      Diagnósticos
      • n_obs = 1.0231023
      • k = 22
      • r2 = 0,07496580.07496580252296514
      • df_resid = 1.0211021
      • se_type = "HC1"
      • n_clusters = —
      Tabelas (1)
      {
        "coefficients": [
          {
            "ci_high": 0.7405963603595828,
            "ci_low": 0.7307198473322746,
            "coef": 0.7356581038459287,
            "p": 0,
            "se": 0.0025165781199505965,
            "t": 292.32476354056934,
            "term": "Intercept"
          },
          {
            "ci_high": 0.04418865211060977,
            "ci_low": 0.02837785283463242,
            "coef": 0.0362832524726211,
            "p": 1.0229997258725272e-18,
            "se": 0.0040286598526058275,
            "t": 9.006283429253099,
            "term": "HASCOMPSTAT"
          }
        ]
      }
      • 62 rows were dropped because one of 'SHAREPT2', 'HASCOMPSTAT' was missing
      Digests dos dados
      ds_0008 773f00f7c7cdb5944fdc06e213f5f7fad37cf5bedba99d13e4199b0e24871e62
      sha256 do resultado
      59a6f2f9e54c7fc172b084ba46f701e8ed9e68e453c77bc62d580ee102030067
    4. 3Turno do modelo
      call_1f230720naive-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.HASCOMPSTAT = 0,03628.",
          "caveats": [],
          "figure_refs": [
            "call_c4d6ac40:coef.HASCOMPSTAT"
          ]
        }
    5. 4Guarda
      resposta aceita
      Números encontrados
      0,03628
      Sem lastro
      —
      Não citados
      —
      1 nova(s) tentativa(s) restante(s)
    6. 5Resposta

      Estimativa ingênua (infer.ols): coef.HASCOMPSTAT = 0,03628.

      Figuras citadas: call_c4d6ac40:coef.HASCOMPSTAT · Parada: answered
      Figura da resposta: call_c4d6ac40:coef.HASCOMPSTAT = 0,03628330.0362832524726211 (se 0,004028660.0040286598526058275)
    7. 6Pontuação
      #0resposta com lastropassouobrigatóriaevery number backed
    8. 7Pontuação
      #1figura da resposta próximafalhouobrigatória0.03628325247 in [-0.03230748536, -0.01363708281]?
    9. 8Pontuação
      #2figura da resposta próximafalhouregistrada0.03628325247 vs -0.02297228408669323
    10. 9Pontuação
      #3figura da resposta próximafalhouregistrada0.004028659853 vs 0.004762857794788859

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

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