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Tarefa crb_papers.q067

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

What is the causal effect of receiving earlier Progresa coverage on precinct-level turnout in the 2000 presidential election for the subpopulation of precincts whose early-coverage status was shifted by the program's enrollment timing?

Resposta

Estimativa ingênua (infer.ols): coef.early_progresa_p = 0,03080.

Valores esperados e de onde vêm

crb_papers.q067.gold

Fonte
CausalReasoningBenchmark, causal_queries.json, index 67 (revision edd0fe1c1d1c)
Tipo
benchmark_gold
Localização
causal_queries.json['67'].effect and .standard_error
Trecho
index 67: effect 0.156302, standard_error 0.0892098
Valor como publicado
0.15630190259612853
Erro-padrão como publicado
0.08920981043805275
Valor
0,1563020.15630190259612853 (se 0,08920980.08920981043805275)
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: 0,1563020.15630190259612853 · relativa 0.000001 · absoluta 8.920981043805274e-8
  • #3 figura da resposta próxima: 0,08920980.08920981043805275 · relativa 0.000001 · absoluta 8.920981043805274e-8

crb_papers.q067.script

Fonte
CausalReasoningBenchmark reference estimation script for index 67
Tipo
software
Localização
solutions/research_papers/IV/Delao2013/question/estimation.py
Valor
—
Conferido por
arquivo do benchmark · sha256 of the script's 844 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
19e2393fffe868856cf7a09f6d3743a583c34f429f1ec8c982b0e1a6862e63ea (identifica a cópia lida, não um arquivo canônico)
Endereço
https://huggingface.co/datasets/syrgkanislab/CausalReasoningBenchmark/resolve/edd0fe1c1d1c5c0a4f0e3e67a50481ac679fb888/solutions/research_papers/IV/Delao2013/question/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
    0.0307999588 in [-0.01854932586, 0.3311531311]?
    obrigatóriapassou[-0.018549325862454863,0.3311531310547119]0,03080.030799958797128487interval · [-0.018549325862454863, 0.3311531310547119]—call_b2acd290
    2figura da resposta próxima
    0.0307999588 vs 0.15630190259612853
    registradafalhou0,1563020.156301902596128530,03080.030799958797128487relativa 0.000001 · absoluta 8.920981043805274e-8crb_papers.q067.goldcall_b2acd290
    3figura da resposta próxima
    0.0451699468 vs 0.08920981043805275
    registradafalhou0,08920980.089209810438052750,04516990.04516994680422397relativa 0.000001 · absoluta 8.920981043805274e-8crb_papers.q067.goldcall_b2acd290

    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.iv_2slsChamada do agente: infer.ols (call_b2acd290)diferente
    Argumentos da referência e do agente
    ArgumentoReferênciaAgente
    cluster——não fixado pela referência
    controls—[]não fixado pela referência
    dataset"ref_iv""ds_0037"diferente
    endog["early_progresa_p"]—ausente
    exog["avgpoverty","pobtot1994","votos_totales1994","pri1994","pan1994","prd1994","villages_2.0","villages_3.0","villages_4.0","villages_5.0","villages_6.0","villages_7.0","villages_8.0","villages_9.0","villages_10.0","villages_11.0","villages_12.0","villages_13.0","villages_14.0"]—ausente
    fe—[]não fixado pela referência
    instruments["treatment"]—ausente
    intercept—truenão fixado pela referência
    level—0,950.95não fixado pela referência
    se"HC1""HC1"igual
    subset——não fixado pela referência
    weights——não fixado pela referência
    x—["early_progresa_p"]não fixado pela referência
    y"t2000""t2000"igual

    Trajetória

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

    1. 0Início

      What is the causal effect of receiving earlier Progresa coverage on precinct-level turnout in the 2000 presidential election for the subpopulation of precincts whose early-coverage status was shifted by the program's enrollment timing?

      ds_0037 (agent)

      | Variable Name | Description | |---------------|-------------| | cve_entIFE | Numeric code for the federal entity (state) as recorded by Mexico's electoral authority (IFE). | | cve_mpioIFE | Numeric municipality code (IFE) corresponding to the locality's municipality. | | seccion | Electoral "sección" (precinct) identifier used by IFE. | | pobtot1994 | Total population in the locality/precinct from the 1994 census (count). | | avgpoverty | Average poverty index for the locality (census-derived index, higher values indicate greater deprivation). | | pobelegiblep | Proportion of the precinct population living in localities deemed eligible for the program (share between 0 and 1). | | treatment | Precinct assignment indicator: 1 when the precinct contains an experimental village assigned to earlier enrollment; 0 for the delayed-assignment comparison group. This is an assignment label, not an indicator that every household or village in the precinct enrolled early. | | numerotreated | Count of evaluation localities within the precinct that were flagged for earlier rollout phases (integer). | | numerocontrol | Count of evaluation localities within the precinct that were flagged for later rollout phases (integer). | | one_random | Legacy sample-selection flag, equal to 1 throughout this extract; it does not give the total number of experimental villages in a precinct. See numerotreated and numerocontrol for the separate counts. | | two_random | Legacy sample-selection flag retained from the source data. It should not be interpreted as a direct count of experimental villages; see numerotreated and numerocontrol. | | early_progresa_p | Number of households enrolled during the first four program-expansion phases divided by the total number of program-enrolled households by the eleventh phase. It measures early enrollment among program recipients, not among all precinct households. | | villages | Total number of localities (villages) aggregated into the electoral precinct (count). | | villages2 | Secondary village-count / grouping field included in the original data (alternate village categorization; sometimes missing). | | pri1994 | Raw count of votes for the PRI in the 1994 election (integer). | | votos_totales1994 | Total number of votes cast in the precinct in the 1994 election (integer). | | pan1994 | Raw count of votes for the PAN in the 1994 election (integer). | | prd1994 | Raw count of votes for the PRD in the 1994 election (integer). | | t2000 | Total votes cast in the precinct in 2000 divided by its voting-age population. Values can exceed 1, for example where special polling places admit voters from outside the precinct. | | t1994 | Total votes cast in the precinct in 1994 divided by its voting-age population; some recorded values exceed 1. | | pri2000s | PRI vote count converted to a share of the voting-age population in 2000 (vote-share metric). | | pri1994s | PRI vote count converted to a share of the voting-age population in 1994 (vote-share metric). | | pan2000s | PAN vote count converted to a share of the voting-age population in 2000 (vote-share metric). | | pan1994s | PAN vote count converted to a share of the voting-age population in 1994 (vote-share metric). | | prd2000s | PRD vote count converted to a share of the voting-age population in 2000 (vote-share metric). | | prd1994s | PRD vote count converted to a share of the voting-age population in 1994 (vote-share metric). | | rep_total | Total number of party observers/representatives recorded at the precinct/polling station (count). | | rep_PAN | Number of PAN party observers present at the precinct (count). | | rep_PRD | Number of PRD party observers present at the precinct (count). | | rep_PRI | Number of PRI party observers present at the precinct (count). | | X_Ivillages_2 | Dummy = 1 if the precinct has 2 villages (indicator capturing number-of-villages category). | | X_Ivillages_3 | Dummy = 1 if the precinct has 3 villages (indicator capturing number-of-villages category). | | X_Ivillages_4 | Dummy = 1 if the precinct has 4 villages (indicator capturing number-of-villages category). | | X_Ivillages_5 | Dummy = 1 if the precinct has 5 villages (indicator capturing number-of-villages category). | | X_Ivillages_6 | Dummy = 1 if the precinct has 6 villages (indicator capturing number-of-villages category). | | X_Ivillages_7 | Dummy = 1 if the precinct has 7 villages (indicator capturing number-of-villages category). | | X_Ivillages_8 | Dummy = 1 if the precinct has 8 villages (indicator capturing number-of-villages category). | | X_Ivillages_9 | Dummy = 1 if the precinct has 9 villages (indicator capturing number-of-villages category). | | X_Ivillages_10 | Dummy = 1 if the precinct has 10 villages (indicator capturing number-of-villages category). | | X_Ivillages_11 | Dummy = 1 if the precinct has 11 villages (indicator capturing number-of-villages category). | | X_Ivillages_12 | Dummy = 1 if the precinct has 12 villages (indicator capturing number-of-villages category). | | X_Ivillages_13 | Dummy = 1 if the precinct has 13 villages (indicator capturing number-of-villages category). | | X_Ivillages_14 | Dummy = 1 if the precinct has 14 villages (indicator capturing number-of-villages category). | Data Description: This precinct-level dataset combines official electoral returns and observer counts from Mexico's Federal Electoral Institute (IFE), population and poverty indicators from census/CONAPO sources, and Progresa program-enrollment records around the late 1990s and the 2000 presidential election. The released file contains 420 precinct observations. The program evaluation initially assigned 506 eligible rural villages in seven Mexican states to different enrollment dates: 320 villages to earlier enrollment beginning in September 1998 and 186 to delayed enrollment beginning in January 2000. Assignment was randomized at the village level. Each village had a 60% probability of assignment to the earlier group. Eligible households in early-assigned villages were offered enrollment; the delayed group began receiving benefits later. Electoral outcomes are recorded for precincts rather than individual villages. Experimental villages were geographically linked to precincts and then to election returns, demographic data, and program-enrollment records. A precinct can contain several villages, including villages outside the experiment. Consequently, assignment and actual coverage are different measures: some households in early-assigned precincts enrolled later, and some households in delayed-assigned precincts enrolled early through the broader rollout. Some released precincts contain both early- and delayed-assigned experimental villages. treatment records the precinct's assignment grouping, while early_progresa_p records actual early coverage among program recipients. t2000 is total votes cast in 2000 divided by the precinct's voting-age population. Geographic identifiers, population and poverty measures, village counts, historical and recent votes, turnout, party-observer counts, and village-count indicators describe the precincts and their composition.

      file 5cd9b9d22abe96d9e8187990bc85716754b5da614d1968270be4026369c4bebb · frame —
      ref_iv (reference)
      file 5cd9b9d22abe96d9e8187990bc85716754b5da614d1968270be4026369c4bebb · frame —
      1. select {"columns":["t2000","early_progresa_p","treatment","avgpoverty","pobtot1994","votos_totales1994","pri1994","pan1994","prd1994","villages"]}
      2. dropna {"columns":["t2000","early_progresa_p","treatment","avgpoverty","pobtot1994","votos_totales1994","pri1994","pan1994","prd1994","villages"]}
      3. dummies {"column":"villages","drop_first":true,"prefix":"villages"}
    2. 1Turno do modelo
      call_9105924dnaive-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_0037",
          "x": [
            "early_progresa_p"
          ],
          "y": "t2000"
        }
    3. 2Ferramenta
      infer.olsv0.1.0call_b2acd290decidido pelo modeloreexecutável

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

      Argumentos
      {
        "cluster": null,
        "controls": [],
        "dataset": "ds_0037",
        "fe": [],
        "intercept": true,
        "level": 0.95,
        "se": "HC1",
        "subset": null,
        "weights": null,
        "x": [
          "early_progresa_p"
        ],
        "y": "t2000"
      }
      Figuras call_b2acd290
      ChaveValorValor brutoErro-padrãoIC
      coef.early_progresa_p
      Coefficient on early_progresa_p in the regression of t2000
      0,0308 0.0307999587971284870,04516990.04516994680422397-0,0579886 – 0,119589
      Diagnósticos
      • n_obs = 420420
      • k = 22
      • r2 = 0,0009403880.0009403880484899485
      • df_resid = 418418
      • se_type = "HC1"
      • n_clusters = —
      Tabelas (1)
      {
        "coefficients": [
          {
            "ci_high": 0.7129930141029328,
            "ci_low": 0.5712573168380652,
            "coef": 0.642125165470499,
            "p": 3.291051001322006e-53,
            "se": 0.03605303673144547,
            "t": 17.81057086130105,
            "term": "Intercept"
          },
          {
            "ci_high": 0.11958851152574872,
            "ci_low": -0.057988593931491736,
            "coef": 0.030799958797128487,
            "p": 0.4956998098691094,
            "se": 0.04516994680422397,
            "t": 0.6818683876388426,
            "term": "early_progresa_p"
          }
        ]
      }
      Digests dos dados
      ds_0037 44fb6dccc6527be61bbc0b5c545fa0d5ff537aa5de9686cecead988ac4dd17b3
      sha256 do resultado
      08443f67328b980ea5f01f3e3cfd6875ff5c0e4af15c89ce67e3c15851d9ea4f
    4. 3Turno do modelo
      call_1452abe5naive-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.early_progresa_p = 0,03080.",
          "caveats": [],
          "figure_refs": [
            "call_b2acd290:coef.early_progresa_p"
          ]
        }
    5. 4Guarda
      resposta aceita
      Números encontrados
      0,03080
      Sem lastro
      —
      Não citados
      —
      1 nova(s) tentativa(s) restante(s)
    6. 5Resposta

      Estimativa ingênua (infer.ols): coef.early_progresa_p = 0,03080.

      Figuras citadas: call_b2acd290:coef.early_progresa_p · Parada: answered
      Figura da resposta: call_b2acd290:coef.early_progresa_p = 0,03080.030799958797128487 (se 0,04516990.04516994680422397)
    7. 6Pontuação
      #0resposta com lastropassouobrigatóriaevery number backed
    8. 7Pontuação
      #1figura da resposta próximapassouobrigatória0.0307999588 in [-0.01854932586, 0.3311531311]?
    9. 8Pontuação
      #2figura da resposta próximafalhouregistrada0.0307999588 vs 0.15630190259612853
    10. 9Pontuação
      #3figura da resposta próximafalhouregistrada0.0451699468 vs 0.08920981043805275

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