Tarefa causcibench_real.q157

Veredito

Pergunta feita ao analista

Do legislators respond differently by race when no partisan signal is included?

Como o agente resolveu e como foi validado

Resumo passo a passo do que o agente executou, por que o caminho está correto e como cada verificação conferiu o resultado.

1. 1Preparação da base de dados
   butler_do_politicians— This study utilizes randomized field experiments to examine the interactions between constituents and state legislators across the United States during October 2008. Legislators across multiple states were sent an email asking for help with the voter registration process for the primary election using black (DeShawn Jackson) or white alias names (Jake Mueller). The sender name, the legislature receiving the email, and the (hypothetical) party affiliation of the sender were randomly assigned. Not all letters included information on which political party's primary election the sender was interested in. Legislative offices were contacted through official email addresses posted on state government websites. The outcome of interest is whether the legislator responded by the November election date or not. Variable Descriptions: leg_party: Political party affiliation of the state legislator (R = Republican, D = Democrat) leg_republican: Binary indicator for legislators affiliated with the Republican Party leg_black: Binary indicator for legislators who identify as Black or African American leg_latino: Binary indicator for legislators who identify as Latino or Hispanic leg_white: Binary indicator for legislators who identify as white leg_notwhite: Binary indicator for legislators who identify as non-white leg_notblackotherminority: Binary indicator for legislators who identify as minorities other than Black (including Latino, Asian American, Native American, and Arab American legislators) reply_atall: Binary indicator for whether the legislative office provided any form of response to the constituent inquiry treat_deshawn: Binary indicator for communications associated with the name ""DeShawn Jackson"" treat_jake: Binary indicator for communications associated with the name ""Jake Mueller"" treat_group: Categorical variable indicating the specific combination of communication characteristics treat_demprimary: Binary indicator for communications that mentioned interest in Democratic primary elections treat_repprimary: Binary indicator for communications that mentioned interest in Republican primary elections treat_noprimary: Binary indicator for communications that did not specify any particular primary election interest treat_primary: Binary indicator for communications that included any reference to primary election participation (either Democratic or Republican)
2. 2Chamada que gerou a resposta finalinfer.ols(Regressão linear por mínimos quadrados ordinários (MQO))citada na respostacoincide com a referência

   Ver chamada call_6782334f nos detalhes

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

   Por que este é o caminho de referência: causcibench_metadata_rule/1: ols
   Argumentos principais

   dataset:

   "butler_do_politicians"

   intercept:

   true

   level:

   0,950.95

   se:

   "HC1"

   x:

   ["treat_deshawn"]

   y:

   "reply_atall"

   Resultado calculado

   • coef.treat_deshawn = -0,0178242 -0.017824238945471624
     Coefficient on treat_deshawn in the regression of reply_atall
   Diagnósticos: n_obs = 4.8594859 (observations used) · k = 22 (parameters estimated, fixed-effect dummies included) · r2 = 0,0003232240.00032322359701220105 · df_resid = 4.8574857 · se_type = "HC1" (heteroskedasticity-robust standard errors (HC1))
3. 3Conferência de lastro e resposta final

   Todos os 1 números escritos na resposta foram conferidos contra as ferramentas e o enunciado (nenhum número sem lastro).

   Números encontrados: -0,01782
   Figuras citadas: call_6782334f:coef.treat_deshawn
   • ✓
     #0resposta com lastro

     every number in the answer was stated by a tool call
   • ✗
     #1figura da resposta próxima

     CauSciBench effect accuracy: |answer figure − gold| ≤ 5% of |gold|

Valores esperados e de onde vêm

Verificações

Caminho de referência e caminho do agente

A chamada de referência ao lado da chamada que deu a resposta, argumento por argumento.

Trajetória

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

1. 0Início

   Do legislators respond differently by race when no partisan signal is included?

   butler_do_politicians (agent)

   This study utilizes randomized field experiments to examine the interactions between constituents and state legislators across the United States during October 2008. Legislators across multiple states were sent an email asking for help with the voter registration process for the primary election using black (DeShawn Jackson) or white alias names (Jake Mueller). The sender name, the legislature receiving the email, and the (hypothetical) party affiliation of the sender were randomly assigned. Not all letters included information on which political party's primary election the sender was interested in. Legislative offices were contacted through official email addresses posted on state government websites. The outcome of interest is whether the legislator responded by the November election date or not. Variable Descriptions: leg_party: Political party affiliation of the state legislator (R = Republican, D = Democrat) leg_republican: Binary indicator for legislators affiliated with the Republican Party leg_black: Binary indicator for legislators who identify as Black or African American leg_latino: Binary indicator for legislators who identify as Latino or Hispanic leg_white: Binary indicator for legislators who identify as white leg_notwhite: Binary indicator for legislators who identify as non-white leg_notblackotherminority: Binary indicator for legislators who identify as minorities other than Black (including Latino, Asian American, Native American, and Arab American legislators) reply_atall: Binary indicator for whether the legislative office provided any form of response to the constituent inquiry treat_deshawn: Binary indicator for communications associated with the name ""DeShawn Jackson"" treat_jake: Binary indicator for communications associated with the name ""Jake Mueller"" treat_group: Categorical variable indicating the specific combination of communication characteristics treat_demprimary: Binary indicator for communications that mentioned interest in Democratic primary elections treat_repprimary: Binary indicator for communications that mentioned interest in Republican primary elections treat_noprimary: Binary indicator for communications that did not specify any particular primary election interest treat_primary: Binary indicator for communications that included any reference to primary election participation (either Democratic or Republican)

   file da070187b161931eb60102008c9fe7907e4660438519fa9b9656a45d317d70df · frame —
2. 1Turno do modelo
   call_fa2e9ca6naive-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": "butler_do_politicians",
       "x": [
         "treat_deshawn"
       ],
       "y": "reply_atall"
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
3. 2Ferramenta
   infer.ols— Regressão linear por mínimos quadrados ordinários (MQO)v0.1.0call_6782334fdecidido pelo modeloreexecutável

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

   Argumentos

   {
     "cluster": null,
     "controls": [],
     "dataset": "butler_do_politicians",
     "fe": [],
     "intercept": true,
     "level": 0.95,
     "se": "HC1",
     "subset": null,
     "weights": null,
     "x": [
       "treat_deshawn"
     ],
     "y": "reply_atall"
   }

   Figuras call_6782334f

   Chave	Valor	Valor bruto	Erro-padrão	IC
   coef.treat_deshawn Coefficient on treat_deshawn in the regression of reply_atall	-0,0178242	-0.017824238945471624	0,01422350.014223500324634713	-0,0457087 – 0,0100603

   Diagnósticos

   • n_obs = 4.8594859 (observations used)
   • k = 22 (parameters estimated, fixed-effect dummies included)
   • r2 = 0,0003232240.00032322359701220105
   • df_resid = 4.8574857
   • se_type = "HC1" (heteroskedasticity-robust standard errors (HC1))
   • n_clusters = — (errors not clustered)

   Derivação matemática e pressupostos

   coef.treat_deshawn

   1. Coeficiente de treat_deshawn na regressão por mínimos quadrados ordinários de reply_atall.→ -0,0178242 (-0.017824238945471624)
   2. Erro-padrão robusto à heterocedasticidade (HC1): a raiz da variância estimada do coeficiente.ep = √V_jj→ 0,0142235 (0.014223500324634713)
   3. Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 4857 graus de liberdade vezes o erro-padrão.IC = β ± c · ep

   Pressupostos do estimador

   • the conditional mean of the outcome is linear in the regressors
   • errors are independent across observations; their variance may differ

   Tabelas (1)

   {
     "coefficients": [
       {
         "ci_high": 0.5939137015970791,
         "ci_low": 0.5545848586661871,
         "coef": 0.5742492801316331,
         "p": 0,
         "se": 0.01003055222763491,
         "t": 57.2500164596655,
         "term": "Intercept"
       },
       {
         "ci_high": 0.010060258213811346,
         "ci_low": -0.045708736104754594,
         "coef": -0.017824238945471624,
         "p": 0.21020992334012087,
         "se": 0.014223500324634713,
         "t": -1.253154184177894,
         "term": "treat_deshawn"
       }
     ]
   }

   Hashes SHA-256 e metadados de reprodução

   Digests dos dados

   butler_do_politicians 0d32e2cb48fbabd40ab0ec59157724749089ca983ed6cba8ad1f10344a49f900

   sha256 do resultado

   556b9cfc2134d01e1ecae2c76aa3055ac38f7c5aa4e1db2d39f4d76d1a39df17

   Resultado mostrado ao modelo
4. 3Turno do modelo
   call_1a3b79bbnaive-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.treat_deshawn = -0,01782.",
       "caveats": [],
       "figure_refs": [
         "call_6782334f:coef.treat_deshawn"
       ]
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
5. 4Guarda
   resposta aceita

   Números encontrados

   -0,01782

   Sem lastro

   —

   Não citados

   —

   1 nova(s) tentativa(s) restante(s)
6. 5Resposta

   Estimativa ingênua (infer.ols): coef.treat_deshawn = -0,01782.

   Figuras citadas: call_6782334f:coef.treat_deshawn · Parada: answered

   Figura da resposta: call_6782334f:coef.treat_deshawn = -0,0178242-0.017824238945471624 (se 0,01422350.014223500324634713)
7. 6Pontuação
   #0resposta com lastropassouobrigatóriaevery number backed
8. 7Pontuação
   #1figura da resposta próximafalhouobrigatória-0.01782423895 vs -0.051

Épocas × verificações

Tentativas

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

• época 1, tentativa 1falhoupontuada(exibindo)

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