Tarefa causcibench_real.q105

Veredito

Pergunta feita ao analista

What was the effect of the radio advertising campaign on the change in the incumbent mayor's vote share in the 2006 elections?

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
   panagopolous_field_experiments— The dataset consists of 49 observations from mayoral elections in U.S. cities, with 33 observations from 2005 and 16 from 2006. The data is gathered through a randomized field experiment. Within matched pairs of municipalities, one city was randomly assigned to the treatment group and the other to the control group. The treatment involved a 60-second, non-partisan get-out-the-vote radio advertisement. The control group received no such intervention. The unit of observation is the city. Inclusion was limited to cities with populations over 30,000 where an incumbent mayor ran for reelection against an opponent. The study's outcome measure is the change in the incumbent mayor's vote share compared to the previous election. Variables: State: State City: City Contested_2006: Mayoral election contested To_Prev: Turnout in prior mayoral election Partisan: Partisan ballot Inc_Voteshare_Prev: Incumbent vote percentage in prior mayoral election Inc_Running_2006: Incumbent running in mayoral election Grp_Buy: Gross Ratings Points purchased (measure of advertising intensity) Statewide_2005: Statewide election Strata90: Low Cost-per-point stratum Strata70: Moderate Cost-per-point stratum Votesharechange: Change in incumbent vote percentage since prior mayoral election Votesharediff: Margin of victory Year_Exp: Experiment year
2. 2Chamada que gerou a resposta finalinfer.ols(Regressão linear por mínimos quadrados ordinários (MQO))citada na resposta

   Ver chamada call_c11c8c22 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:

   "panagopolous_field_experiments"

   intercept:

   true

   level:

   0,950.95

   se:

   "HC1"

   x:

   ["Grp_Buy"]

   y:

   "Votesharechange"

   Resultado calculado

   • coef.Grp_Buy = -0,0426033 -0.04260334859042887
     Coefficient on Grp_Buy in the regression of Votesharechange
   Diagnósticos: n_obs = 5353 (observations used) · k = 22 (parameters estimated, fixed-effect dummies included) · r2 = 0,007010140.007010143972914773 · df_resid = 5151 · 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: -0,04260
   Figuras citadas: call_c11c8c22:coef.Grp_Buy
   • ✓
     #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

   What was the effect of the radio advertising campaign on the change in the incumbent mayor's vote share in the 2006 elections?

   panagopolous_field_experiments (agent)

   The dataset consists of 49 observations from mayoral elections in U.S. cities, with 33 observations from 2005 and 16 from 2006. The data is gathered through a randomized field experiment. Within matched pairs of municipalities, one city was randomly assigned to the treatment group and the other to the control group. The treatment involved a 60-second, non-partisan get-out-the-vote radio advertisement. The control group received no such intervention. The unit of observation is the city. Inclusion was limited to cities with populations over 30,000 where an incumbent mayor ran for reelection against an opponent. The study's outcome measure is the change in the incumbent mayor's vote share compared to the previous election. Variables: State: State City: City Contested_2006: Mayoral election contested To_Prev: Turnout in prior mayoral election Partisan: Partisan ballot Inc_Voteshare_Prev: Incumbent vote percentage in prior mayoral election Inc_Running_2006: Incumbent running in mayoral election Grp_Buy: Gross Ratings Points purchased (measure of advertising intensity) Statewide_2005: Statewide election Strata90: Low Cost-per-point stratum Strata70: Moderate Cost-per-point stratum Votesharechange: Change in incumbent vote percentage since prior mayoral election Votesharediff: Margin of victory Year_Exp: Experiment year

   file 4752df417db23bf5ef74fdf35be37e544f94fd7b8c6eb3769e309feea61b4e65 · frame —
2. 1Turno do modelo
   call_96bada77naive-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": "panagopolous_field_experiments",
       "x": [
         "Grp_Buy"
       ],
       "y": "Votesharechange"
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
3. 2Ferramenta
   infer.ols— Regressão linear por mínimos quadrados ordinários (MQO)v0.1.0call_c11c8c22decidido pelo modeloreexecutável

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

   Argumentos

   {
     "cluster": null,
     "controls": [],
     "dataset": "panagopolous_field_experiments",
     "fe": [],
     "intercept": true,
     "level": 0.95,
     "se": "HC1",
     "subset": null,
     "weights": null,
     "x": [
       "Grp_Buy"
     ],
     "y": "Votesharechange"
   }

   Figuras call_c11c8c22

   Chave	Valor	Valor bruto	Erro-padrão	IC
   coef.Grp_Buy Coefficient on Grp_Buy in the regression of Votesharechange	-0,0426033	-0.04260334859042887	0,06852230.0685222973267414	-0,180168 – 0,0949609

   Diagnósticos

   • n_obs = 5353 (observations used)
   • k = 22 (parameters estimated, fixed-effect dummies included)
   • r2 = 0,007010140.007010143972914773
   • df_resid = 5151
   • se_type = "HC1" (heteroskedasticity-robust standard errors (HC1))
   • n_clusters = — (errors not clustered)

   Derivação matemática e pressupostos

   coef.Grp_Buy

   1. Coeficiente de Grp_Buy na regressão por mínimos quadrados ordinários de Votesharechange.→ -0,0426033 (-0.04260334859042887)
   2. Erro-padrão robusto à heterocedasticidade (HC1): a raiz da variância estimada do coeficiente.ep = √V_jj→ 0,0685223 (0.0685222973267414)
   3. Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 51 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": 9.9210421842342,
         "ci_low": -2.357887263344104,
         "coef": 3.7815774604450474,
         "p": 0.22191388527383332,
         "se": 3.058136260397882,
         "t": 1.2365627749866976,
         "term": "Intercept"
       },
       {
         "ci_high": 0.09496090342749335,
         "ci_low": -0.18016760060835108,
         "coef": -0.04260334859042887,
         "p": 0.5368795898988691,
         "se": 0.0685222973267414,
         "t": -0.6217443117424868,
         "term": "Grp_Buy"
       }
     ]
   }

   • 25 rows were dropped because one of 'Votesharechange', 'Grp_Buy' was missing

   Hashes SHA-256 e metadados de reprodução

   Digests dos dados

   panagopolous_field_experiments 8731b90da302477ec1e88876005d7d126e6877c1a1e0aac2702cbb54eac9b602

   sha256 do resultado

   1ab0e64f0ff162479dfb128eacf28019109b7fc3074015de5d30f81c81fe9122

   Resultado mostrado ao modelo
4. 3Turno do modelo
   call_cf2f6c56naive-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.Grp_Buy = -0,04260.",
       "caveats": [],
       "figure_refs": [
         "call_c11c8c22:coef.Grp_Buy"
       ]
     }

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

   Números encontrados

   -0,04260

   Sem lastro

   —

   Não citados

   —

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

   Estimativa ingênua (infer.ols): coef.Grp_Buy = -0,04260.

   Figuras citadas: call_c11c8c22:coef.Grp_Buy · Parada: answered

   Figura da resposta: call_c11c8c22:coef.Grp_Buy = -0,0426033-0.04260334859042887 (se 0,06852230.0685222973267414)
7. 6Pontuação
   #0resposta com lastropassouobrigatóriaevery number backed
8. 7Pontuação
   #1figura da resposta próximafalhouobrigatória-0.04260334859 vs -0.112

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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