Tarefa causcibench_real.q023

Veredito

Pergunta feita ao analista

If a Democrat won the previous congressional election in a district closely, is it likely that a Democrat will win the current election in that same district?

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
   lee_do_voters— This dataset is used to study whether elected officials moderate their policy positions in response to closely contested elections. The goal is to test whether policy outcomes reflect the preferences of a nearly divided electorate (convergence) or the ideology of the winning candidate (divergence). Legislative behavior is measured using the Americans for Democratic Action (ADA) score. For each congressional session, the ADA selects a set of key roll-call votes and assigns each representative a score from 0 to 100. Higher scores indicate a more liberal voting record. The dataset includes the following variables: state: ICPSR state code identifying the state district: Congressional district number id: Unique election identifier year: Year of the election score: ADA voting score (higher values indicate more liberal voting) demvoteshare: Share of votes received by the Democratic candidate (between 0 and 1) democrat: Indicator equal to 1 if the Democrat won the election, i.e., secured more than 50% of the vote lagdemvoteshare: Democratic vote share in the previous election (between 0 and 1) lagdemocrat: Indicator equal to 1 if the Democrat won the previous election, i.e., secured more than 50% of the vote
2. 2Chamada que gerou a resposta finalinfer.rdd(Regressão descontínua (RDD))citada na resposta

   Ver chamada call_36a4f39b nos detalhes

   O tratamento muda num limiar da variável de corte: regressão descontínua.

   Argumentos principais

   cutoff:

   00

   dataset:

   "lee_do_voters"

   kernel:

   "triangular"

   level:

   0,950.95

   polynomial:

   11

   running:

   "score"

   y:

   "Unnamed: 0"

   Resultado calculado

   • effect = 279,165 279.1647312977128
     Jump in Unnamed: 0 at the cutoff of score
   Diagnósticos: bandwidth = 14,208614.20859737632235 (half-width of the window around the cutoff (Imbens–Kalyanaraman)) · bandwidth_method = "IK" · n_left = 758758 (observations with positive weight below the cutoff) · n_right = 3.1803180 (observations with positive weight at or above the cutoff) · kernel = "triangular" · polynomial = 11 · bandwidth_sensitive = true (the estimate at half or double the bandwidth falls outside the main interval: the result depends on the window) · n_obs = 13.58813588 (rows used to choose the bandwidth) · se_type = "HC1" (heteroskedasticity-robust standard errors (HC1))
3. 3Conferência de lastro e resposta final

   Todos os 4 números escritos na resposta foram conferidos contra as ferramentas e o enunciado (nenhum número sem lastro).

   Números encontrados: 279,2 · 95% · -207,4 · 765,7
   Figuras citadas: call_36a4f39b:effect
   • ✓
     #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.

Nenhum caminho de referência registrado para esta tarefa.

Trajetória

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

1. 0Início

   If a Democrat won the previous congressional election in a district closely, is it likely that a Democrat will win the current election in that same district?

   lee_do_voters (agent)

   This dataset is used to study whether elected officials moderate their policy positions in response to closely contested elections. The goal is to test whether policy outcomes reflect the preferences of a nearly divided electorate (convergence) or the ideology of the winning candidate (divergence). Legislative behavior is measured using the Americans for Democratic Action (ADA) score. For each congressional session, the ADA selects a set of key roll-call votes and assigns each representative a score from 0 to 100. Higher scores indicate a more liberal voting record. The dataset includes the following variables: state: ICPSR state code identifying the state district: Congressional district number id: Unique election identifier year: Year of the election score: ADA voting score (higher values indicate more liberal voting) demvoteshare: Share of votes received by the Democratic candidate (between 0 and 1) democrat: Indicator equal to 1 if the Democrat won the election, i.e., secured more than 50% of the vote lagdemvoteshare: Democratic vote share in the previous election (between 0 and 1) lagdemocrat: Indicator equal to 1 if the Democrat won the previous election, i.e., secured more than 50% of the vote

   file 93bbbf2757b5a306889b643aaee68f5e1ce9b42f12b64ff95b9aceb040ea8d5a · frame —
2. 1Turno do modelo
   call_18935c05rules-1Fim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

   Texto visível

   O tratamento muda num limiar da variável de corte: regressão descontínua.

   Chamadas pedidas

   • infer.rdd

     {
       "cutoff": 0,
       "dataset": "lee_do_voters",
       "running": "score",
       "y": "Unnamed: 0"
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
3. 2Ferramenta
   infer.rdd— Regressão descontínua (RDD)v0.1.0call_36a4f39bdecidido pelo modeloreexecutável

   Motivo: O tratamento muda num limiar da variável de corte: regressão descontínua.

   Argumentos

   {
     "bandwidth": null,
     "cluster": null,
     "cutoff": 0,
     "dataset": "lee_do_voters",
     "kernel": "triangular",
     "level": 0.95,
     "polynomial": 1,
     "running": "score",
     "subset": null,
     "y": "Unnamed: 0"
   }

   Figuras call_36a4f39b

   Chave	Valor	Valor bruto	Erro-padrão	IC
   effect Jump in Unnamed: 0 at the cutoff of score	279,165	279.1647312977128	248,26248.25990671589517	-207,416 – 765,745

   Diagnósticos

   • bandwidth = 14,208614.20859737632235 (half-width of the window around the cutoff (Imbens–Kalyanaraman))
   • bandwidth_method = "IK"
   • n_left = 758758 (observations with positive weight below the cutoff)
   • n_right = 3.1803180 (observations with positive weight at or above the cutoff)
   • kernel = "triangular"
   • polynomial = 11
   • bandwidth_sensitive = true (the estimate at half or double the bandwidth falls outside the main interval: the result depends on the window)
   • n_obs = 13.58813588 (rows used to choose the bandwidth)
   • se_type = "HC1" (heteroskedasticity-robust standard errors (HC1))

   Derivação matemática e pressupostos

   effect

   1. Ajuste linear ponderado (kernel triangular) de cada lado do limiar, com janela h (Imbens–Kalyanaraman); o efeito é a diferença entre os dois ajustes no limiar.→ 279,165 (279.1647312977128)
   2. Erro-padrão robusto à heterocedasticidade (HC1): a raiz da variância estimada do coeficiente.ep = √V_jj→ 248,26 (248.25990671589517)
   3. Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição normal vezes o erro-padrão.IC = τ ± c · ep

   Pressupostos do estimador

   • units cannot precisely control the running variable around the cutoff
   • every other determinant of the outcome is continuous at the cutoff

   Tabelas (1)

   {
     "sensitivity": [
       {
         "bandwidth": 7.104298688161175,
         "ci_high": 174.66283692489503,
         "ci_low": -1229.7032357853918,
         "estimate": -527.5201994302485,
         "multiplier": 0.5,
         "n_left": 575,
         "n_right": 1459,
         "se": 358.2632343726077
       },
       {
         "bandwidth": 14.20859737632235,
         "ci_high": 765.7452072661408,
         "ci_low": -207.41574467071518,
         "estimate": 279.1647312977128,
         "multiplier": 1,
         "n_left": 758,
         "n_right": 3180,
         "se": 248.25990671589517
       },
       {
         "bandwidth": 28.4171947526447,
         "ci_high": 878.495478407755,
         "ci_low": 96.1918375711154,
         "estimate": 487.3436579894352,
         "multiplier": 2,
         "n_left": 819,
         "n_right": 5048,
         "se": 199.5709224780024
       }
     ]
   }

   Hashes SHA-256 e metadados de reprodução

   Digests dos dados

   lee_do_voters 4736dd24ef756d8688297bf924dd17c7367e5e6d2a5eb23cf41140965280d20b

   sha256 do resultado

   5b0621a72dc9138bb42644832ee90a112afe14c9b76c47c9aa5c63fa485ad439

   Resultado mostrado ao modelo
4. 3Turno do modelo
   call_50e0b4ecrules-1Fim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

   Chamadas pedidas

   • submit_answer (meta-ferramenta)

     {
       "answer": "Estimativa (infer.rdd): effect = 279,2 (IC 95%: -207,4 a 765,7).",
       "caveats": [],
       "figure_refs": [
         "call_36a4f39b:effect"
       ]
     }

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

   Números encontrados

   279,2 · 95% · -207,4 · 765,7

   Sem lastro

   —

   Não citados

   —

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

   Estimativa (infer.rdd): effect = 279,2 (IC 95%: -207,4 a 765,7).

   Figuras citadas: call_36a4f39b:effect · Parada: answered

   Figura da resposta: call_36a4f39b:effect = 279,165279.1647312977128 (se 248,26248.25990671589517)
7. 6Pontuação
   #0resposta com lastropassouobrigatóriaevery number backed
8. 7Pontuação
   #1figura da resposta próximafalhouobrigatória279.1647313 vs 0.167

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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