Pular para o conteúdo

Tarefa causcibench_real.q050

causcibench_realagentT2publicépoca 1, tentativa 1Execução: 20261015T171059Z-suite-causcibench_real-scripted-ded01d

Veredito

passouPassou em todas as verificações obrigatórias.

Família do método (das chamadas de ferramentas): did (OLS with unit and time fixed effects; the regressor varies within unit)

Pergunta feita ao analista

What is the effect of CompStat adoption on the number of Part 2 arrests?

Resposta

Estimativa (infer.ols): coef.HASCOMPSTAT = 20471.

Valores esperados e de onde vêm

causcibench_real.gold.q050

Fonte
CauSciBench real split, data/metadata_json/real_input.json at d9bb43a, id 50
Tipo
benchmark_gold
Localização
real_input.json, record id 50: fields effect and std_error
Trecho
id 50 effect 20471.057 std_error 6339.787
Valor como publicado
20471.057
Erro-padrão como publicado
6339.787
Valor
20.471,120471.057 (se 6.339,796339.787)
Conferido por
arquivo do benchmark · Written by scripts/freeze_causcibench_real.py from the pinned file; equal to the `answer` and `std_error` of the same id in data/metadata_csv/real_input.csv at the pin.
sha256 da cópia lida
c1f4c8930675afff63d816d8ed066941ae92697e7bfc31e9d38519398dd80438 (identifica a cópia lida, não um arquivo canônico)
Endereço
https://raw.githubusercontent.com/causalNLP/CauSciBench/d9bb43a2d90f261978cc5ac0b628c0484f6e8956/data/metadata_json/real_input.json
Licença
CauSciBench repository (MIT for code; metadata as published in the repository)
  • #1 figura da resposta próxima: 20.471,120471.057 · relativa 0.05

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
20471.05742 vs 20471.057
obrigatóriapassou20.471,120471.05720.471,120471.05741625657relativa 0.05causcibench_real.gold.q050call_3d1b4154

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 · metadata_rule

Referência: infer.olsChamada do agente: infer.ols (call_3d1b4154)igual
Argumentos da referência e do agente
ArgumentoReferênciaAgente
cluster——não fixado pela referência
controls["POP"]["POP"]igual
dataset"eckhouse_metrics_management""eckhouse_metrics_management"igual
fe["AGENCY","YEAR"]["AGENCY","YEAR"]igual
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["HASCOMPSTAT"]["HASCOMPSTAT"]igual
y"PART2arrests""PART2arrests"igual

Trajetória

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

  1. 0Início

    What is the effect of CompStat adoption on the number of Part 2 arrests?

    eckhouse_metrics_management (agent)

    This dataset is an agency-year panel that links police-reported crime, clearances, and unfounded counts to population, mortality, and local political context, tracking the timing and status of metrics-focused management practices within U.S. law enforcement agencies. The unit of observation is the police agency by calendar year, identified by a standard agency code and geographic identifiers. The variables are: AGENCY: Agency name used in reporting YEAR: Calendar year of the observation for the agency ORI: Agency's Originating Agency Identifier used in federal crime reporting systems STATE: State of the agency's jurisdiction COUNTY: County of the agency's jurisdiction POP: Jurisdiction population for the agency-year (denominator for rate variables) COMPSTAT: Indicator for adoption of a metrics-focused management program by the agency COMPSTATDATE: Date (or year) of the agency's program adoption as recorded in the source HASCOMPSTAT: Indicator equal to 1 in years at or after adoption, and 0 otherwise YEAR.UNFOUNDED.MURDER: Count of homicides deemed unfounded in the agency-year YEAR.UNFOUNDED.RAPE.TOT: Count of rapes deemed unfounded in the agency-year YEAR.UNFOUND.AUTO.THFT: Count of motor vehicle thefts deemed unfounded in the agency-year YEAR.ACT.NUM.MURDER: Count of homicide offenses known to the agency in the year (UCR ""actual"" offenses) YEAR.ACT.NUM.RAPE.TOT: Count of rape offenses known to the agency in the year YEAR.ACT.NUM.ASSAULT.TOT: Count of aggravated assault offenses known to the agency in the year YEAR.ACT.VHC.THFT.TOT: Count of motor vehicle theft offenses known to the agency in the year YEAR.ACT.ALL.FIELDS: Total ""actual"" offenses across included categories in the agency-year YEAR.TOT.CLR.MURDER: Number of homicide offenses cleared (by arrest or exceptional means) in the agency-year YEAR.TOT.CLR.RAPE.TOT: Number of rape offenses cleared in the agency-year YEAR.TOT.CLR.ASSAULT.TOT: Number of aggravated assaults cleared in the agency-year; YEAR.TOT.CLR.VHC.THFT.TOT: Number of motor vehicle thefts cleared in the agency-year YEAR.TOT.CLR.ALL.FIELDS: Total clearances across included categories in the agency-year SHAREPT2: Share of Part II arrests in total arrests (proportion) clearrate: Overall clearance rate across included offense categories (clearances divided by offenses) unfoundrapepct: Percent of reported rapes deemed unfounded murderclearrate: Clearance rate for homicide rapeclearrate: Clearance rate for rape assaultclearrate: Clearance rate for aggravated assault unfoundmurderpct: Percent of reported homicides deemed unfounded unfoundassaultpct: Percent of reported aggravated assaults deemed unfounded PART1arrests: Count of arrests for UCR Part I offenses in the agency-year PART2arrests: Count of arrests for UCR Part II offenses in the agency-year popblackpct: Percent of the jurisdiction's population identified as Black/African American popwhitepct: Percent of the jurisdiction's population identified as White unfoundautopct: Percent of reported motor vehicle thefts deemed unfounded yearssincecompstat: Years elapsed since the agency's adoption year (constructed relative to COMPSTATDATE) FIPS_PLACE_ID: FIPS place identifier for the agency's primary jurisdiction cdcHomicide: Cause-specific mortality measure for homicide matched to the agency's geography (level/units follow source coding) cdcSuicide: Cause-specific mortality measure for suicide matched to the agency's geography (level/units follow source coding) cdcUndetermined: Cause-specific mortality measure for undetermined intent matched to the agency's geography (level/units follow source coding) cdcUnintentional: Cause-specific mortality measure for unintentional injury matched to the agency's geography (level/units follow source coding) haselection: Indicator equal to 1 if a relevant local election occurred for the agency's jurisdiction in that year years_since_election: Years since the most recent relevant local election for the agency's jurisdiction partisantotal: Summary measure of local political partisanship for the jurisdiction (construction follows source coding) incidentsperpop: Total incidents per population (offenses divided by POP), a per-capita incident rate autoclearrate: Clearance rate for motor vehicle theft YEAR.ACT.NUM.KILLINGS: Count of killings as coded in the source (maintained separately from homicide for internal consistency and checks)

    file b303608e3fc216acfb6520b968a7a6de354eb1248fdd53ac70671f7eb04f1298 · frame —
  2. 1Turno do modelo
    call_ae2a7f77referenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache
    Texto visível

    causcibench_metadata_rule/1: did

    Chamadas pedidas
    • infer.ols
      {
        "controls": [
          "POP"
        ],
        "dataset": "eckhouse_metrics_management",
        "fe": [
          "AGENCY",
          "YEAR"
        ],
        "x": [
          "HASCOMPSTAT"
        ],
        "y": "PART2arrests"
      }
  3. 2Ferramenta
    infer.olsv0.1.0call_3d1b4154decidido pelo modeloreexecutável

    Motivo: causcibench_metadata_rule/1: did

    Argumentos
    {
      "cluster": null,
      "controls": [
        "POP"
      ],
      "dataset": "eckhouse_metrics_management",
      "fe": [
        "AGENCY",
        "YEAR"
      ],
      "intercept": true,
      "level": 0.95,
      "se": "HC1",
      "subset": null,
      "weights": null,
      "x": [
        "HASCOMPSTAT"
      ],
      "y": "PART2arrests"
    }
    Figuras call_3d1b4154
    ChaveValorValor brutoErro-padrãoIC
    coef.HASCOMPSTAT
    Coefficient on HASCOMPSTAT in the regression of PART2arrests
    20.471,1 20471.057416256576.658,556658.5539548095977.404,86 – 33.537,3
    Diagnósticos
    • n_obs = 1.0811081
    • k = 7272
    • r2 = 0,6300990.6300993423273054
    • df_resid = 1.0091009
    • se_type = "HC1"
    • n_clusters = —
    • fixed_effects = "AGENCY (47 levels), YEAR (24 levels)"
    Tabelas (1)
    {
      "coefficients": [
        {
          "ci_high": 184969.32723625033,
          "ci_low": 80960.74323936789,
          "coef": 132965.03523780912,
          "p": 6.191006261001499e-7,
          "se": 26501.4617543785,
          "t": 5.017271744108266,
          "term": "Intercept"
        },
        {
          "ci_high": 33537.25683433522,
          "ci_low": 7404.857998177922,
          "coef": 20471.05741625657,
          "p": 0.0021659751936975303,
          "se": 6658.553954809597,
          "t": 3.0743998704808795,
          "term": "HASCOMPSTAT"
        },
        {
          "ci_high": -0.17759882445915823,
          "ci_low": -0.43003684328917813,
          "coef": -0.30381783387416816,
          "p": 0.0000026469661928991627,
          "se": 0.06432138814211119,
          "t": -4.72343403414919,
          "term": "POP"
        }
      ]
    }
    • 4 rows were dropped because one of 'PART2arrests', 'HASCOMPSTAT', 'POP', 'AGENCY', 'YEAR' was missing
    Digests dos dados
    eckhouse_metrics_management edcc0e3e260047813f052e191a75de4b0bf34c47415a9adb49cf78c6244589b6
    sha256 do resultado
    9bbf2335ce0b5f05192c2703fd1e007a84cfe56abd346f3525b8c68016dcd315
  4. 3Turno do modelo
    call_186d7e4freferenceFim: 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.HASCOMPSTAT = 20471.",
        "caveats": [],
        "figure_refs": [
          "call_3d1b4154:coef.HASCOMPSTAT"
        ]
      }
  5. 4Guarda
    resposta aceita
    Números encontrados
    20471
    Sem lastro
    —
    Não citados
    —
    1 nova(s) tentativa(s) restante(s)
  6. 5Resposta

    Estimativa (infer.ols): coef.HASCOMPSTAT = 20471.

    Figuras citadas: call_3d1b4154:coef.HASCOMPSTAT · Parada: answered
    Figura da resposta: call_3d1b4154:coef.HASCOMPSTAT = 20.471,120471.05741625657 (se 6.658,556658.553954809597)
  7. 6Pontuação
    #0resposta com lastropassouobrigatóriaevery number backed
  8. 7Pontuação
    #1figura da resposta próximapassouobrigatória20471.05742 vs 20471.057

Épocas × verificações

Resultado de cada verificação em cada época pontuada
#01
Época 1passoupassou

Tentativas

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

Downloads desta tarefa

Pacote da tarefa (.zip)
101,7 KB · sha256 6e1f994d1de8ebe435402d47385093db5ffd533ebb440076a669f04c0e4f4ef4

O hash de um zip é só de transporte: o que ancora a integridade é o RUN_SHA256SUMS que vai dentro dele, cópia exata do SHA256SUMS da execução.

Downloads desta tarefa
ArquivoTamanhosha256
attachments/09c556a32a0dd975829084c7b947d463255ce7d35b822ba73002c0764b8d44b2.json1,2 KB09c556a32a0dd975829084c7b947d463255ce7d35b822ba73002c0764b8d44b2
attachments/433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420.json2,4 KB433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420
attachments/52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b.json64,0 KB52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b
attachments/7a8188b9c208aab706ed0862cc67139cf91487eff41a51f4de07896e1b777b74.json6,2 KB7a8188b9c208aab706ed0862cc67139cf91487eff41a51f4de07896e1b777b74
attachments/ab431ff78797e30c55beed27acc5db083f7d9ea7bb7bee032e64c3d983159766.json245 Bab431ff78797e30c55beed27acc5db083f7d9ea7bb7bee032e64c3d983159766
attachments/f09b9b422dcff5e6d63acea512ab68d2542604b8ee0210d4adc7899eede9c43e.json97 Bf09b9b422dcff5e6d63acea512ab68d2542604b8ee0210d4adc7899eede9c43e
attachments/f8fed0558424fe7247b41492a4fc7c52fa1953615bf0d4508c854e5c08c8973b.json1,3 KBf8fed0558424fe7247b41492a4fc7c52fa1953615bf0d4508c854e5c08c8973b
native/causcibench_real/causcibench_real.q050.e1.a1.json1,8 KB3193148c280ec61c27b5790dbdd47969315736ed118258a2449ff822262291a8
sources/causcibench_real.gold.q050.json1,2 KB18b959265f05e9567d7bcf05f9dcdd440cffb6616bb310308ddf5d9c3480da29
specs/causcibench_real.q050.json8,5 KB0f8ea40f4013920ac0881f19c1f14892f3b37c9789053cd0492c543b365f3f2e
traces/causcibench_real.q050.e1.a1.jsonl17,5 KB879ea7cdb1525917cb0bce5a4c4d2f721200404af8769ca6dc9ea95e2a2051ec
trajectories/causcibench_real.q050.e1.a1.jsonl13,3 KB29f3db0094849f45baf08a56c81f24d07fd36c174c0d238f40909a4f8fe51d6a