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Tarefa crb_papers.q076

crb_papersagentT2publicépoca 1, tentativa 1Execução: 20261015T170925Z-suite-crb_papers-scripted-2dbcf5

Veredito

passouPassou em todas as verificações obrigatórias.

Família do método (das chamadas de ferramentas): iv (two-stage least squares)

Pergunta feita ao analista

What is the causal effect of being enrolled in public health insurance (versus only private insurance) on an individual's support for the Affordable Care Act, measured for the subgroup whose insurance enrollment was shifted by the exogenous eligibility variation exploited in this analysis?

Resposta

Estimativa (infer.iv_2sls): coef.privpubins3r = 0,04599. Reference path transcribed from the item's own estimation script.

Valores esperados e de onde vêm

crb_papers.q076.gold

Fonte
CausalReasoningBenchmark, causal_queries.json, index 76 (revision edd0fe1c1d1c)
Tipo
benchmark_gold
Localização
causal_queries.json['76'].effect and .standard_error
Trecho
index 76: effect 0.0459886, standard_error 0.022872
Valor como publicado
0.045988589833671156
Erro-padrão como publicado
0.022871988293883778
Valor
0,04598860.045988589833671156 (se 0,0228720.022871988293883778)
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: 0,04598860.045988589833671156 · relativa 0.000001 · absoluta 2.2871988293883776e-8
  • #3 figura da resposta próxima: 0,0228720.022871988293883778 · relativa 0.000001 · absoluta 2.2871988293883776e-8

crb_papers.q076.script

Fonte
CausalReasoningBenchmark reference estimation script for index 76
Tipo
software
Localização
solutions/research_papers/IV/Lerman2017/question/estimation.py
Valor
—
Conferido por
arquivo do benchmark · sha256 of the script's 1209 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
4335bae30bcef81c583e4890d4f0ece94c311d206c73d9547f1666a19b9211e0 (identifica a cópia lida, não um arquivo canônico)
Endereço
https://huggingface.co/datasets/syrgkanislab/CausalReasoningBenchmark/resolve/edd0fe1c1d1c5c0a4f0e3e67a50481ac679fb888/solutions/research_papers/IV/Lerman2017/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
    0.04598858983 in [0.001159492778, 0.09081768689]?
    obrigatóriapassou[0.0011594927776589492,0.09081768688968336]0,04598860.04598858983362574interval · [0.0011594927776589492, 0.09081768688968336]—call_85585a75
    2figura da resposta próxima
    0.04598858983 vs 0.045988589833671156
    registradapassou0,04598860.0459885898336711560,04598860.04598858983362574relativa 0.000001 · absoluta 2.2871988293883776e-8crb_papers.q076.goldcall_85585a75
    3figura da resposta próxima
    0.02294266585 vs 0.022871988293883778
    registradafalhou0,0228720.0228719882938837780,02294270.022942665851068515relativa 0.000001 · absoluta 2.2871988293883776e-8crb_papers.q076.goldcall_85585a75

    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.iv_2sls (call_85585a75)igual
    Argumentos da referência e do agente
    ArgumentoReferênciaAgente
    cluster——não fixado pela referência
    dataset"ref_iv""ref_iv"igual
    endog["privpubins3r"]["privpubins3r"]igual
    exog["rep","ind","con","mod","ideostrength","hcsocial","fininsur","healthcaresupport","child18","male","married","labor","mobility","homeowner","religimp","employed","votereg","vote08","black","hispanic2","military","fincome","newsint","publicemp","bornagain"]["rep","ind","con","mod","ideostrength","hcsocial","fininsur","healthcaresupport","child18","male","married","labor","mobility","homeowner","religimp","employed","votereg","vote08","black","hispanic2","military","fincome","newsint","publicemp","bornagain"]igual
    instruments["byr4647"]["byr4647"]igual
    level—0,950.95não fixado pela referência
    se"HC1""HC1"igual
    subset——não fixado pela referência
    y"suppafford""suppafford"igual

    Pendências do caminho de referência: needs_op:iv_se_hc0

    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 being enrolled in public health insurance (versus only private insurance) on an individual's support for the Affordable Care Act, measured for the subgroup whose insurance enrollment was shifted by the exogenous eligibility variation exploited in this analysis?

      ds_0046 (agent)

      | Variable Name | Description | | --- | --- | | row_id | Row identifier / case index (sequential record number). | | educ | Education level (ordered categorical from the CCES; coded 1–6 with higher values indicating more education). | | votereg | Voter registration status (binary indicator; 1 = registered to vote, 0 = not registered). | | child18 | Child under age 18 present in household (binary indicator; 1 = yes, 0 = no). | | newsint | How closely respondent follows news about government and public affairs (ordinal scale; CCES item typically coded 1–4 with higher values = follows more closely). | | suppafford | Support for the Affordable Care Act (ACA) (dichotomous; 1 = support, 0 = oppose). | | dontcutmedicare | Budget-priority item: respondent selected “Don’t cut domestic spending such as Medicare and Social Security” as the option they would least prefer among deficit-reduction choices (dichotomous; 1 = selected this option, 0 = otherwise). | | byr4647 | Indicator for birth cohort in this extract: 1 = born in 1946–1947, 0 = born in 1948–1949 (no other birth years included in this file). | | privpubins3r | Health insurance type (recoded dichotomy used in the study): 1 = has any public insurance (including public-only or public + private), 0 = private insurance only. (Respondents with other/none/unknown coverage may appear as missing, depending on CCES source coding.) | | con | Ideology: conservative (binary indicator; 1 = self-identifies as conservative, 0 = otherwise). | | mod | Ideology: moderate (binary indicator; 1 = self-identifies as moderate, 0 = otherwise). | | ideostrength | Strength of ideological identification (ordinal measure; higher values indicate stronger ideology). | | rep | Party identification: Republican (binary indicator; 1 = Republican, 0 = otherwise). | | ind | Party identification: Independent (binary indicator; 1 = Independent, 0 = otherwise). | | employed | Employment status (binary indicator; 1 = employed full-time or part-time, 0 = otherwise). | | healthcaresupport | Occupation indicator: works in “healthcare support” occupation category (binary indicator; 1 = yes, 0 = no). | | hcsocial | Industry indicator: works in “healthcare or social assistance” industry category (binary indicator; 1 = yes, 0 = no). | | fininsur | Industry indicator: works in “finance or insurance” industry category (binary indicator; 1 = yes, 0 = no). | | hispanic2 | Hispanic ethnicity (binary indicator; 1 = Hispanic, 0 = not Hispanic). | | black | Race: Black/African American (binary indicator; 1 = Black, 0 = not Black). | | military | Military household affiliation (binary indicator; 1 = respondent or household member is affiliated with the military, 0 = otherwise). | | mobility | Residential mobility (binary indicator; 1 = recently moved, 0 = otherwise; exact reference period follows CCES coding). | | married | Marital status (binary indicator; 1 = married, 0 = not married). | | homeowner | Homeownership (binary indicator; 1 = owns home, 0 = does not own). | | labor | Labor union household indicator (binary indicator; 1 = respondent or someone in household belongs to a labor union, 0 = otherwise). | | publicemp | Public-sector employment (binary indicator; 1 = public employee, 0 = otherwise). | | vote08 | Self-reported voting in the 2008 election (binary indicator; 1 = voted, 0 = did not vote). | | bornagain | Religious identification as “born again” (binary indicator; 1 = yes, 0 = no). | | religimp | Importance of religion (ordinal scale; higher values indicate religion is more important to the respondent). | | male | Gender (binary indicator; 1 = male, 0 = not male). | | fincome | Family income (ordered categorical bins from the CCES; coded 1–16 with higher values indicating higher income; some respondents may have missing/refused). | Dataset Context: This file is an analysis-ready extract derived from the 2012 Cooperative Congressional Election Study (CCES), an online national survey administered by YouGov/Polymetrix. It contains individual-level responses covering demographic characteristics (education, income, sex, race/ethnicity, marital and homeownership status), political identifiers and behavior (party identification, ideology and its strength, voter registration and 2008 turnout, news consumption), work/household indicators (employment, public-employee and military household flags, union household, mobility), religiosity measures, and items about health insurance source and attitudes toward health-related policy questions. Variable codings mix binary indicators and ordered categorical scales; the table above summarizes each column as present in the provided CSV. This extract is restricted to respondents born in 1946–1949 (corresponding to roughly ages 63–66 at the time of the October–November 2012 CCES field period).

      file bb1eb952988798d36682e2840f8a80022fb48ddf3c93591bfca5793089f1ee30 · frame —
      ref_iv (reference)
      file bb1eb952988798d36682e2840f8a80022fb48ddf3c93591bfca5793089f1ee30 · frame —
      1. select {"columns":["suppafford","privpubins3r","byr4647","rep","ind","con","mod","ideostrength","hcsocial","fininsur","healthcaresupport","child18","male","married","labor","mobility","homeowner","religimp","employed","votereg","vote08","black","hispanic2","military","fincome","newsint","publicemp","bornagain"]}
      2. dropna {"columns":["suppafford","privpubins3r","byr4647","rep","ind","con","mod","ideostrength","hcsocial","fininsur","healthcaresupport","child18","male","married","labor","mobility","homeowner","religimp","employed","votereg","vote08","black","hispanic2","military","fincome","newsint","publicemp","bornagain"]}
    2. 1Transformação
      ref_iv · select {"columns":["suppafford","privpubins3r","byr4647","rep","ind","con","mod","ideostrength","hcsocial","fininsur","healthcaresupport","child18","male","married","labor","mobility","homeowner","religimp","employed","votereg","vote08","black","hispanic2","military","fincome","newsint","publicemp","bornagain"]}
      5221 → 5221 linhas
      Frame antes → depois: 689c88964b32 → dfea1bcb946f
    3. 2Transformação
      ref_iv · dropna {"columns":["suppafford","privpubins3r","byr4647","rep","ind","con","mod","ideostrength","hcsocial","fininsur","healthcaresupport","child18","male","married","labor","mobility","homeowner","religimp","employed","votereg","vote08","black","hispanic2","military","fincome","newsint","publicemp","bornagain"]}
      5221 → 4389 linhas
      Frame antes → depois: dfea1bcb946f → c6be60d3c844
    4. 3Turno do modelo
      call_75d477cereferenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache
      Texto visível

      2SLS by matrices: X = [1, controls, D], Z = [1, controls, byr4647]

      Chamadas pedidas
      • infer.iv_2sls
        {
          "dataset": "ref_iv",
          "endog": [
            "privpubins3r"
          ],
          "exog": [
            "rep",
            "ind",
            "con",
            "mod",
            "ideostrength",
            "hcsocial",
            "fininsur",
            "healthcaresupport",
            "child18",
            "male",
            "married",
            "labor",
            "mobility",
            "homeowner",
            "religimp",
            "employed",
            "votereg",
            "vote08",
            "black",
            "hispanic2",
            "military",
            "fincome",
            "newsint",
            "publicemp",
            "bornagain"
          ],
          "instruments": [
            "byr4647"
          ],
          "se": "HC1",
          "y": "suppafford"
        }
    5. 4Ferramenta
      infer.iv_2slsv0.1.0call_85585a75decidido pelo modeloreexecutável

      Motivo: 2SLS by matrices: X = [1, controls, D], Z = [1, controls, byr4647]

      Argumentos
      {
        "cluster": null,
        "dataset": "ref_iv",
        "endog": [
          "privpubins3r"
        ],
        "exog": [
          "rep",
          "ind",
          "con",
          "mod",
          "ideostrength",
          "hcsocial",
          "fininsur",
          "healthcaresupport",
          "child18",
          "male",
          "married",
          "labor",
          "mobility",
          "homeowner",
          "religimp",
          "employed",
          "votereg",
          "vote08",
          "black",
          "hispanic2",
          "military",
          "fincome",
          "newsint",
          "publicemp",
          "bornagain"
        ],
        "instruments": [
          "byr4647"
        ],
        "level": 0.95,
        "se": "HC1",
        "subset": null,
        "y": "suppafford"
      }
      Figuras call_85585a75
      ChaveValorValor brutoErro-padrãoIC
      coef.privpubins3r
      2SLS effect of privpubins3r on suppafford
      0,0459886 0.045988589833625740,02294270.0229426658510685150,00100931 – 0,0909679
      Diagnósticos
      • first_stage_F.privpubins3r = 1.186,481186.4814767053833
      • partial_r2.privpubins3r = 0,2252660.22526566832883976
      • weak_instrument = false
      • n_obs = 4.3894389
      • se_type = "HC1"
      • n_clusters = —
      Tabelas (3)
      {
        "coefficients": [
          {
            "ci_high": 1.1998909964894866,
            "ci_low": 0.926913068832784,
            "coef": 1.0634020326611353,
            "p": 2.359497378462159e-51,
            "se": 0.06961918291453165,
            "t": 15.274554916374505,
            "term": "Intercept"
          },
          {
            "ci_high": -0.495227190530027,
            "ci_low": -0.5760152604390468,
            "coef": -0.535621225484537,
            "p": 1.2150185662878503e-138,
            "se": 0.020603861508470666,
            "t": -25.99615733508654,
            "term": "rep"
          },
          {
            "ci_high": -0.3193219109194207,
            "ci_low": -0.41737871604836413,
            "coef": -0.3683503134838924,
            "p": 5.832571745269853e-48,
            "se": 0.025008009661761674,
            "t": -14.729293473007408,
            "term": "ind"
          },
          {
            "ci_high": -0.2539152786701208,
            "ci_low": -0.3380440911957779,
            "coef": -0.29597968493294935,
            "p": 2.1026790052511775e-42,
            "se": 0.021455870948554532,
            "t": -13.794811016650401,
            "term": "con"
          },
          {
            "ci_high": -0.08950809913534243,
            "ci_low": -0.16548773510375142,
            "coef": -0.12749791711954692,
            "p": 5.271408053407336e-11,
            "se": 0.019377538029068976,
            "t": -6.579675752837253,
            "term": "mod"
          },
          {
            "ci_high": -0.023799983805385613,
            "ci_low": -0.06651587886441755,
            "coef": -0.045157931334901584,
            "p": 0.00003459957081950421,
            "se": 0.010894088533094136,
            "t": -4.145177560997463,
            "term": "ideostrength"
          },
          {
            "ci_high": 0.04330111509311052,
            "ci_low": -0.029954252899757502,
            "coef": 0.00667343109667651,
            "p": 0.7209612823186354,
            "se": 0.018682751779772275,
            "t": 0.35719743939978915,
            "term": "hcsocial"
          },
          {
            "ci_high": 0.06755168298222652,
            "ci_low": -0.02875979705399398,
            "coef": 0.019395942964116272,
            "p": 0.429778505762183,
            "se": 0.024562889032710648,
            "t": 0.7896442042418748,
            "term": "fininsur"
          },
          {
            "ci_high": 0.07848860352225805,
            "ci_low": -0.0300072727010153,
            "coef": 0.024240665410621375,
            "p": 0.3810499709912437,
            "se": 0.027670347991503596,
            "t": 0.8760520618701532,
            "term": "healthcaresupport"
          },
          {
            "ci_high": 0.12063984800406702,
            "ci_low": 0.009957185485303534,
            "coef": 0.06529851674468527,
            "p": 0.020755253235667594,
            "se": 0.02822805709424167,
            "t": 2.3132487130333077,
            "term": "child18"
          },
          {
            "ci_high": 0.004938916397677375,
            "ci_low": -0.038269735559102167,
            "coef": -0.016665409580712394,
            "p": 0.13052475033333047,
            "se": 0.011019759252669051,
            "t": -1.5123206595167615,
            "term": "male"
          },
          {
            "ci_high": 0.029369727192432676,
            "ci_low": -0.020050337160948725,
            "coef": 0.004659695015741976,
            "p": 0.7116218564108542,
            "se": 0.012603892664145614,
            "t": 0.369702848152416,
            "term": "married"
          },
          {
            "ci_high": 0.031911457957140385,
            "ci_low": -0.009288532236595445,
            "coef": 0.011311462860272471,
            "p": 0.28175632439739384,
            "se": 0.010507478307849842,
            "t": 1.0765154615472292,
            "term": "labor"
          },
          {
            "ci_high": 0.011447647618552322,
            "ci_low": -0.011459149112593818,
            "coef": -0.0000057507470207482415,
            "p": 0.9992146312579826,
            "se": 0.005842056481640604,
            "t": -0.0009843703221324009,
            "term": "mobility"
          },
          {
            "ci_high": 0.03975195461408698,
            "ci_low": -0.02275380942450612,
            "coef": 0.008499072594790432,
            "p": 0.5939563440236985,
            "se": 0.015941216409584395,
            "t": 0.5331508196376096,
            "term": "homeowner"
          },
          {
            "ci_high": 0.013461018742039324,
            "ci_low": -0.006526006645719105,
            "coef": 0.003467506048160109,
            "p": 0.4963831490140381,
            "se": 0.005097409846768527,
            "t": 0.6802486267331073,
            "term": "religimp"
          },
          {
            "ci_high": 0.030447759190340443,
            "ci_low": -0.021396970028213037,
            "coef": 0.0045253945810637035,
            "p": 0.7321751008035118,
            "se": 0.013222269351974897,
            "t": 0.3422555130740688,
            "term": "employed"
          },
          {
            "ci_high": 0.04113345080323666,
            "ci_low": -0.15480895577251588,
            "coef": -0.05683775248463961,
            "p": 0.2554403152270909,
            "se": 0.04997235623118302,
            "t": -1.1373838812341721,
            "term": "votereg"
          },
          {
            "ci_high": 0.02898792463357916,
            "ci_low": -0.09673732412912617,
            "coef": -0.033874699747773504,
            "p": 0.29081827482476463,
            "se": 0.03206445724649732,
            "t": -1.0564563587451314,
            "term": "vote08"
          },
          {
            "ci_high": 0.09972560967053624,
            "ci_low": 0.029277983038917058,
            "coef": 0.06450179635472665,
            "p": 0.0003342144680385824,
            "se": 0.017966676816922877,
            "t": 3.5900794015492155,
            "term": "black"
          },
          {
            "ci_high": 0.029475281510118384,
            "ci_low": -0.06600558093961459,
            "coef": -0.018265149714748102,
            "p": 0.45324913333898564,
            "se": 0.024351051694131296,
            "t": -0.7500764215103728,
            "term": "hispanic2"
          },
          {
            "ci_high": 0.027648295974859727,
            "ci_low": -0.019624362742575393,
            "coef": 0.004011966616142166,
            "p": 0.7393228987168285,
            "se": 0.012056227045009358,
            "t": 0.33277132233528306,
            "term": "military"
          },
          {
            "ci_high": 0.004946478130924055,
            "ci_low": -0.00297960798505229,
            "coef": 0.0009834350729358828,
            "p": 0.6266350250532322,
            "se": 0.002021436838653274,
            "t": 0.48650299338121733,
            "term": "fincome"
          },
          {
            "ci_high": 0.05439265333713929,
            "ci_low": 0.015324156191139152,
            "coef": 0.03485840476413922,
            "p": 0.0004725906551866571,
            "se": 0.009963870970636913,
            "t": 3.498480145604595,
            "term": "newsint"
          },
          {
            "ci_high": 0.024319297497587603,
            "ci_low": -0.021328381830185057,
            "coef": 0.0014954578337012746,
            "p": 0.8977941122499721,
            "se": 0.011641798895699327,
            "t": 0.1284559067803277,
            "term": "publicemp"
          },
          {
            "ci_high": 0.006313201045687406,
            "ci_low": -0.046633669284156534,
            "coef": -0.020160234119234562,
            "p": 0.13551483727184058,
            "se": 0.013503354948466975,
            "t": -1.4929796481076236,
            "term": "bornagain"
          },
          {
            "ci_high": 0.09096786937094442,
            "ci_low": 0.0010093102963070547,
            "coef": 0.04598858983362574,
            "p": 0.04507815077608663,
            "se": 0.022942665851068515,
            "t": 2.004500703281781,
            "term": "privpubins3r"
          }
        ],
        "first_stage": [
          {
            "ci_high": 0.46375996555239035,
            "ci_low": 0.4138116658017809,
            "coef": 0.43878581567708563,
            "endog": "privpubins3r",
            "instrument": "byr4647",
            "p": 3.367367806798428e-230,
            "se": 0.012738611676254891,
            "t": 34.44534042080849
          }
        ],
        "reduced_form": [
          {
            "ci_high": 0.03987690122823495,
            "ci_low": 0.00048138057573781853,
            "coef": 0.020179140901986383,
            "instrument": "byr4647",
            "p": 0.04466025568095804,
            "se": 0.010047273718659813,
            "t": 2.008419544150534
          }
        ]
      }
      Digests dos dados
      ref_iv c6be60d3c844bbd766dd5bb0d1810d6f2df234c1defba10fd9e06e0b2ed42610
      sha256 do resultado
      74efc30767c964c8635c415fa8efef250c5969f45e22bab410cbe8f65ae3b110
    6. 5Turno do modelo
      call_8b0d1e5freferenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache
      Chamadas pedidas
      • submit_answer (meta-ferramenta)
        {
          "answer": "Estimativa (infer.iv_2sls): coef.privpubins3r = 0,04599. Reference path transcribed from the item's own estimation script.",
          "caveats": [],
          "figure_refs": [
            "call_85585a75:coef.privpubins3r"
          ]
        }
    7. 6Guarda
      resposta aceita
      Números encontrados
      0,04599
      Sem lastro
      —
      Não citados
      —
      1 nova(s) tentativa(s) restante(s)
    8. 7Resposta

      Estimativa (infer.iv_2sls): coef.privpubins3r = 0,04599. Reference path transcribed from the item's own estimation script.

      Figuras citadas: call_85585a75:coef.privpubins3r · Parada: answered
      Figura da resposta: call_85585a75:coef.privpubins3r = 0,04598860.04598858983362574 (se 0,02294270.022942665851068515)
    9. 8Pontuação
      #0resposta com lastropassouobrigatóriaevery number backed
    10. 9Pontuação
      #1figura da resposta próximapassouobrigatória0.04598858983 in [0.001159492778, 0.09081768689]?
    11. 10Pontuação
      #2figura da resposta próximapassouregistrada0.04598858983 vs 0.045988589833671156
    12. 11Pontuação
      #3figura da resposta próximafalhouregistrada0.02294266585 vs 0.022871988293883778

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