Pular para o conteúdo

Tarefa causcibench_real.q028

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 (two-group two-period difference-in-differences)

Pergunta feita ao analista

Did an increase in minimum wage decrease employment?

Resposta

Estimativa (infer.did_2x2): did = 2,754.

Valores esperados e de onde vêm

causcibench_real.gold.q028

Fonte
CauSciBench real split, data/metadata_json/real_input.json at d9bb43a, id 28
Tipo
benchmark_gold
Localização
real_input.json, record id 28: fields effect and std_error
Trecho
id 28 effect 2.754 std_error 1.688
Valor como publicado
2.754
Erro-padrão como publicado
1.688
Valor
2,7542.754 (se 1,6881.688)
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: 2,7542.754 · 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
2.753605783 vs 2.754
obrigatóriapassou2,7542.7542,753612.7536057829805816relativa 0.05causcibench_real.gold.q028call_9fa80e13

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.did_2x2Chamada do agente: infer.did_2x2 (call_9fa80e13)igual
Argumentos da referência e do agente
ArgumentoReferênciaAgente
cluster——não fixado pela referência
controls[][]igual
dataset"card_minimum_wages""card_minimum_wages"igual
level—0,950.95não fixado pela referência
post"post""post"igual
treated"state""state"igual
y"fte""fte"igual

Trajetória

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

  1. 0Início

    Did an increase in minimum wage decrease employment?

    card_minimum_wages (agent)

    The dataset was collected to examine the effects of a minimum wage increase on fast food employment. It includes two survey waves from 410 fast food restaurants such as Burger King, KFC, Wendy's, and Roy Rogers in New Jersey and Pennsylvania. The first wave took place just before the April 1992 wage increase, and the second occurred seven to eight months later. The survey gathered information on employment, wages, prices, store characteristics, and location. Data collection involved phone interviews. The fast food sector was selected for its high share of low-wage jobs, standardization, and compliance with wage laws. The variables are: sheet: unique store identifier chain: chain identifier (1 = Burger King, 2 = KFC, 3 = Roy Rogers, 4 = Wendy's) co_owned: 1 if the store is company-owned, 0 otherwise state: 1 if the store is in New Jersey, 0 if in Pennsylvania after: 1 if the data was collected after an increase in minimum wage, 0 otherwise fte: number of full time employees southj: 1 if the store is in southern New Jersey centralj: 1 if the store is in central New Jersey northj: 1 if the store is in northern New Jersey pa1: 1 if the store is in northeast suburbs of Philadelphia pa2: 1 if the store is in Easton or nearby areas in Pennsylvania shore: 1 if the store is on the New Jersey shore ncalls: number of callbacks before completing the first interview empft: number of full-time employees (first interview) emppt: number of part-time employees (first interview) nmgrs: number of managers or assistant managers (first interview) wage_st: starting wage in dollars per hour (first interview) inctime: months until usual first raise (first interview) firstinc: amount of usual first raise in dollars per hour (first interview) bonus: 1 if the store offers a cash bonus for new workers pctaff: percent of employees affected by the new minimum wage meals: code for meal benefits (0 = none, 1 = free, 2 = reduced price, 3 = both) open: hour the store opens (first interview) hrsopen: number of hours the store is open per day (first interview) psoda: price of a medium soda, including tax (first interview) pfry: price of small fries, including tax (first interview) pentree: price of an entree including tax (first interview) nregs: number of cash registers in the store (first interview) nregs11: number of registers open at 11:00 am (first interview) type2: mode of second interview (1 = phone, 2 = personal) status2: status of second interview (0 = refused, 1 = answered, 2-5 = closed for various reasons) date2: date of second interview (mmddyy format) ncalls2: number of callbacks before completing the second interview empft2: number of full-time employees (second interview) emppt2: number of part-time employees (second interview) nmgrs2: number of managers or assistant managers (second interview) wage_st2: starting wage in dollars per hour (second interview) inctime2: months until usual first raise (second interview) firstin2: amount of usual first raise in dollars per hour (second interview) special2: 1 if the store has a special program for new workers (second interview) meals2: code for meal benefits (second interview; same coding as meals) open2r: hour the store opens (second interview) hrsopen2: number of hours the store is open per day (second interview) psoda2: price of a medium soda, including tax (second interview) pfry2: price of small fries, including tax (second interview) pentree2: price of an entree including tax (second interview) nregs2: number of cash registers in the store (second interview) nregs112: number of registers open at 11:00 am (second interview)

    file d60fdf3cbe6dc79b86bff0c6499806848793e9b6a642643cd9d5fbb390a80732 · frame —
  2. 1Turno do modelo
    call_e766cfd9referenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache
    Texto visível

    causcibench_metadata_rule/1: did

    Chamadas pedidas
    • infer.did_2x2
      {
        "controls": [],
        "dataset": "card_minimum_wages",
        "post": "post",
        "treated": "state",
        "y": "fte"
      }
  3. 2Ferramenta
    infer.did_2x2v0.1.0call_9fa80e13decidido pelo modeloreexecutável

    Motivo: causcibench_metadata_rule/1: did

    Argumentos
    {
      "cluster": null,
      "controls": [],
      "dataset": "card_minimum_wages",
      "level": 0.95,
      "post": "post",
      "treated": "state",
      "y": "fte"
    }
    Figuras call_9fa80e13
    ChaveValorValor brutoErro-padrãoIC
    did
    Difference-in-differences effect on fte
    2,75361 2.75360578298058161,795451.7954508096057529-0,770813 – 6,27802
    Diagnósticos
    • design = "stacked"
    • n_obs = 794794
    • se_type = "HC1"
    • parallel_trends = "untestable"
    • n_clusters = —
    Tabelas (1)
    {
      "means": [
        {
          "group": "treated",
          "mean": 20.439408099688475,
          "n": 321,
          "period": "before"
        },
        {
          "group": "treated",
          "mean": 21.02742946708464,
          "n": 319,
          "period": "after"
        },
        {
          "group": "comparison",
          "mean": 23.33116883116883,
          "n": 77,
          "period": "before"
        },
        {
          "group": "comparison",
          "mean": 21.165584415584416,
          "n": 77,
          "period": "after"
        }
      ]
    }
    • 26 rows were dropped because one of 'fte', 'state', 'post' was missing
    • with two periods parallel trends cannot be tested
    Digests dos dados
    card_minimum_wages 9ecd0139eedcfa298eb4f431b84bcea62de46362ee01b1f2e959eaddda54d225
    sha256 do resultado
    65d4160e7bd20426a45f52b9c189b04843d5e74b21dcb24b4ea84386a60401a8
  4. 3Turno do modelo
    call_f8033038referenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache
    Chamadas pedidas
    • submit_answer (meta-ferramenta)
      {
        "answer": "Estimativa (infer.did_2x2): did = 2,754.",
        "caveats": [],
        "figure_refs": [
          "call_9fa80e13:did"
        ]
      }
  5. 4Guarda
    resposta aceita
    Números encontrados
    2,754
    Sem lastro
    —
    Não citados
    —
    1 nova(s) tentativa(s) restante(s)
  6. 5Resposta

    Estimativa (infer.did_2x2): did = 2,754.

    Figuras citadas: call_9fa80e13:did · Parada: answered
    Figura da resposta: call_9fa80e13:did = 2,753612.7536057829805816 (se 1,795451.7954508096057529)
  7. 6Pontuação
    #0resposta com lastropassouobrigatóriaevery number backed
  8. 7Pontuação
    #1figura da resposta próximapassouobrigatória2.753605783 vs 2.754

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)
99,8 KB · sha256 0a1911b6c6ea9351220718889af7c0ba986db46227d1f03b484fd63d7d230afa

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/191f8a0eb285302562592e487eb98d678d122a6dffc75b861e98577613f825b2.json215 B191f8a0eb285302562592e487eb98d678d122a6dffc75b861e98577613f825b2
attachments/2e124368dd62547fce47b9ca358251bc8c287725befe410aa0c459806cdeac48.json77 B2e124368dd62547fce47b9ca358251bc8c287725befe410aa0c459806cdeac48
attachments/433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420.json2,4 KB433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420
attachments/52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b.json64,0 KB52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b
attachments/82678a5866d3b4f50c1c65e2a37bc5a059f922a8d2a5bc60e442141adf3bcf3c.json5,1 KB82678a5866d3b4f50c1c65e2a37bc5a059f922a8d2a5bc60e442141adf3bcf3c
attachments/98c3e2ce3d4d85dd2770faea91629bf64812eb18f0900b8d5515d01b9716eb0e.json1,1 KB98c3e2ce3d4d85dd2770faea91629bf64812eb18f0900b8d5515d01b9716eb0e
attachments/b4ef6d630faa677b5b81cece4aa1f48feab92f0fbe51638e8857854570aa1243.json1,0 KBb4ef6d630faa677b5b81cece4aa1f48feab92f0fbe51638e8857854570aa1243
native/causcibench_real/causcibench_real.q028.e1.a1.json1,7 KB957465d78a70d575ccabb55934528713c26a4c7df7cb324aef558538eb4a24d7
sources/causcibench_real.gold.q028.json1,2 KB9135ce8795c4aede3360641c4781706caa6e75811fd389ae5bbc9b5551e7b091
specs/causcibench_real.q028.json7,4 KB5c85b652852df88b9de234a229b87bdd1ce0f63810c1acba59bec751bec241a4
traces/causcibench_real.q028.e1.a1.jsonl16,0 KBafd9b5e4d683046933259ab2a8612e758f007f8256cee9cceb522e2873d4c04e
trajectories/causcibench_real.q028.e1.a1.jsonl12,2 KB957e3c536331dd3fd9f735dae5f8b909c288b37870fb891845d3d5aed64076da