Tarefa causcibench_real.q105
causcibench_realagentT2publicépoca 1, tentativa 1Execução: 20261015T171059Z-suite-causcibench_real-scripted-ded01dVeredito
-0.0687810151 vs -0.112Família do método (das chamadas de ferramentas): Regressão linear por mínimos quadrados ordinários (MQO) · infer.ols (ols · regressão linear)
coef.Grp_Buy =-0,068781-0.06878101509503858coef.Grp_Buy =-0,112-0.112Pergunta 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?
Estimativa (infer.ols): coef.Grp_Buy = -0,06878.
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.
- 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 - 2Chamada que gerou a resposta finalVer chamada call_8d989945 nos detalhes
infer.ols(Regressão linear por mínimos quadrados ordinários (MQO))citada na respostacoincide com a referênciacauscibench_metadata_rule/1: ols
Por que este é o caminho de referência: causcibench_metadata_rule/1: olsArgumentos principais- controls:
["Strata70","Strata90","Partisan","To_Prev","Statewide_2005"]- dataset:
"panagopolous_field_experiments"- intercept:
true- level:
- 0,95
0.95 - se:
"HC1"- x:
["Grp_Buy"]- y:
"Votesharechange"
Resultado calculadocoef.Grp_Buy= -0,068781-0.06878101509503858Coefficient on Grp_Buy in the regression of Votesharechange
Diagnósticos:n_obs= 5353(observations used) ·k= 77(parameters estimated, fixed-effect dummies included) ·r2= 0,1581430.15814342299656214·df_resid= 4646·se_type="HC1"(heteroskedasticity-robust standard errors (HC1)) - 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:-0,06878Figuras citadas:call_8d989945:coef.Grp_Buy- #0resposta com lastroevery number in the answer was stated by a tool call
- #1figura da resposta próximaCauSciBench effect accuracy: |answer figure − gold| ≤ 5% of |gold|
Aprofundamento técnico para especialistas
Acesso direto às fontes primárias, tabela formal de verificações, comparação de argumentos, trajetória completa com prompts brutos e somas SHA-256.
Valores esperados e de onde vêm
causcibench_real.gold.q105
- Fonte
- CauSciBench real split, data/metadata_json/real_input.json at d9bb43a, id 105
- Tipo
- benchmark_gold
- Localização
- real_input.json, record id 105: fields effect and std_error
- Trecho
id 105 effect -0.112 std_error 0.136
- Valor como publicado
-0.112- Erro-padrão como publicado
0.136- Valor
- -0,112
-0.112(se 0,1360.136) - Conferido por
- arquivo do benchmark · Written by scripts/freeze_causcibench_real.py from the pinned file; equal to the `answer` and `std_error` of the same id in data/metadata_csv/real_input.csv at the pin.
- sha256 da cópia lida
c1f4c8930675afff63d816d8ed066941ae92697e7bfc31e9d38519398dd80438(identifica a cópia lida, não um arquivo canônico)- Endereço
https://raw.githubusercontent.com/causalNLP/CauSciBench/d9bb43a2d90f261978cc5ac0b628c0484f6e8956/data/metadata_json/real_input.json- Licença
- CauSciBench repository (MIT for code; metadata as published in the repository)
- #1 figura da resposta próxima: -0,112
-0.112· relativa 0.05
Especificação técnica do gabarito
CauSciBench real_input.json @ d9bb43a, id 105: effect -0.112
Verificações
| # | Verificação | Obrigatória | Resultado | Esperado | Observado | Tolerância | Fonte | Chamada |
|---|---|---|---|---|---|---|---|---|
| 0 | resposta com lastro every number in the answer was stated by a tool call every number backed | obrigatória | passou | 0 sem lastro | 0 sem lastro[] | — | — | — |
| 1 | figura da resposta próxima CauSciBench effect accuracy: |answer figure − gold| ≤ 5% of |gold| -0.0687810151 vs -0.112 | obrigatória | falhou | -0,112-0.112 | -0,068781-0.06878101509503858 | relativa 0.05 | causcibench_real.gold.q105 | call_8d989945 |
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 · metadata_rule
| Argumento | Referência | Agente | |
|---|---|---|---|
| cluster | — | — | não fixado pela referência |
| controls | ["Strata70","Strata90","Partisan","To_Prev","Statewide_2005"] | ["Strata70","Strata90","Partisan","To_Prev","Statewide_2005"] | igual |
| dataset | "panagopolous_field_experiments" | "panagopolous_field_experiments" | igual |
| fe | — | [] | não fixado pela referência |
| intercept | — | true | não fixado pela referência |
| level | — | 0,950.95 | não fixado pela referência |
| se | — | "HC1" | não fixado pela referência |
| subset | — | — | não fixado pela referência |
| weights | — | — | não fixado pela referência |
| x | ["Grp_Buy"] | ["Grp_Buy"] | igual |
| y | "Votesharechange" | "Votesharechange" | igual |
Trajetória
Cada evento na ordem em que aconteceu: turnos do modelo, chamadas de ferramentas, guardas, transformações e pontuação.
- 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
file4752df417db23bf5ef74fdf35be37e544f94fd7b8c6eb3769e309feea61b4e65· frame — - 1Turno do modelo
call_63704c6dreferenceFim:tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cacheTexto visívelcauscibench_metadata_rule/1: ols
Chamadas pedidasinfer.ols{ "controls": [ "Strata70", "Strata90", "Partisan", "To_Prev", "Statewide_2005" ], "dataset": "panagopolous_field_experiments", "x": [ "Grp_Buy" ], "y": "Votesharechange" }
- 2Ferramenta
infer.ols— Regressão linear por mínimos quadrados ordinários (MQO)v0.1.0call_8d989945decidido pelo modeloreexecutávelMotivo: causcibench_metadata_rule/1: ols
Argumentos
{ "cluster": null, "controls": [ "Strata70", "Strata90", "Partisan", "To_Prev", "Statewide_2005" ], "dataset": "panagopolous_field_experiments", "fe": [], "intercept": true, "level": 0.95, "se": "HC1", "subset": null, "weights": null, "x": [ "Grp_Buy" ], "y": "Votesharechange" }Figuras call_8d989945 Chave Valor Valor bruto Erro-padrão IC coef.Grp_Buy Coefficient on Grp_Buy in the regression of Votesharechange-0,068781 -0.068781015095038580,0685031 0.0685031201868404-0,206671 – 0,0691086 Diagnósticosn_obs= 5353(observations used)k= 77(parameters estimated, fixed-effect dummies included)r2= 0,1581430.15814342299656214df_resid= 4646se_type="HC1"(heteroskedasticity-robust standard errors (HC1))n_clusters= — (errors not clustered)
Derivação matemática e pressupostos
coef.Grp_Buy- Coeficiente de Grp_Buy na regressão por mínimos quadrados ordinários de Votesharechange.→ -0,068781 (-0.06878101509503858)
- Erro-padrão robusto à heterocedasticidade (HC1): a raiz da variância estimada do coeficiente.
ep = √V_jj→ 0,0685031 (0.0685031201868404) - Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 46 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": 15.478169463009623, "ci_low": -14.666319978194794, "coef": 0.4059247424074152, "p": 0.9570016797058446, "se": 7.487842255054536, "t": 0.054211177076200136, "term": "Intercept" }, { "ci_high": 0.06910861404130118, "ci_low": -0.20667064423137832, "coef": -0.06878101509503858, "p": 0.3206039425084569, "se": 0.0685031201868404, "t": -1.0040566751914397, "term": "Grp_Buy" }, { "ci_high": 3.7044927218590793, "ci_low": -23.51396577063332, "coef": -9.90473652438712, "p": 0.14972919781164404, "se": 6.761020916113067, "t": -1.4649764654302215, "term": "Strata70" }, { "ci_high": 19.68477869305086, "ci_low": 1.097100526448287, "coef": 10.390939609749573, "p": 0.02924056091309772, "se": 4.617149090241164, "t": 2.2505098723608317, "term": "Strata90" }, { "ci_high": 11.85233673354172, "ci_low": -12.001487759052033, "coef": -0.07457551275515736, "p": 0.9900124788827185, "se": 5.9252512910751705, "t": -0.01258605063172354, "term": "Partisan" }, { "ci_high": 0.5445083343558024, "ci_low": -0.3842386402641344, "coef": 0.08013484704583396, "p": 0.7299079291931829, "se": 0.23069924121214, "t": 0.3473563529068818, "term": "To_Prev" }, { "ci_high": 17.136528247523465, "ci_low": -13.256377023544713, "coef": 1.940075611989376, "p": 0.798340950909941, "se": 7.549548344033298, "t": 0.2569790302121442, "term": "Statewide_2005" } ] }- 25 rows were dropped because one of 'Votesharechange', 'Grp_Buy', 'Strata70', 'Strata90', 'Partisan', 'To_Prev', 'Statewide_2005' was missing
Hashes SHA-256 e metadados de reprodução
- Digests dos dados
panagopolous_field_experiments8731b90da302477ec1e88876005d7d126e6877c1a1e0aac2702cbb54eac9b602- sha256 do resultado
2655db21a25262a566b0bb6ae41075a7e48de0e16bc39bd3377bec694ed6d183
- 3Turno do modelo
call_819a29aereferenceFim:tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cacheChamadas pedidassubmit_answer(meta-ferramenta){ "answer": "Estimativa (infer.ols): coef.Grp_Buy = -0,06878.", "caveats": [], "figure_refs": [ "call_8d989945:coef.Grp_Buy" ] }
- 4Guardaresposta aceita
- Números encontrados
- -0,06878
- Sem lastro
- —
- Não citados
- —
1 nova(s) tentativa(s) restante(s) - 5Resposta
Estimativa (infer.ols): coef.Grp_Buy = -0,06878.
Figuras citadas:call_8d989945:coef.Grp_Buy· Parada:answeredFigura da resposta:call_8d989945:coef.Grp_Buy= -0,068781-0.06878101509503858(se 0,06850310.0685031201868404) - 6Pontuação#0resposta com lastropassouobrigatória
every number backed - 7Pontuação#1figura da resposta próximafalhouobrigatória
-0.0687810151 vs -0.112
Épocas × verificações
| # | 0 | 1 |
|---|---|---|
| Época 1 | passou | falhou |
Tentativas
Cada tentativa é seu próprio arquivo e é publicada; só a última de cada época é pontuada.
- época 1, tentativa 1falhoupontuada(exibindo)
Downloads desta tarefa
c4937568ffae0e584348fd139d55e09211ec393c2db1f1b615c35ea0fae2ace0O hash de um zip é só de transporte: o que ancora a integridade é o RUN_SHA256SUMS que vai dentro dele, cópia exata do SHA256SUMS da execução.
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| native/causcibench_real/causcibench_real.q105.e1.a1.json | 1,8 KB | a960c5512e965fbdb8eb67aee0ffd1a045d75ba907d150ba68eb14daa72011ea |
| sources/causcibench_real.gold.q105.json | 1,2 KB | 37ab5f33e994eba953bb68656d1670a2f58d85c2ba5ef721d8fd637587563c0a |
| specs/causcibench_real.q105.json | 5,6 KB | c9d8d3e7c0770bbbb4a26acdb147e9a4eb17e32efe24ed6b015b9b6d64dfde69 |
| traces/causcibench_real.q105.e1.a1.jsonl | 13,9 KB | a8473e5575a8ca05996b1b7226e8294ce59882ead09a6f8b4dfbd2d909ba4a3e |
| trajectories/causcibench_real.q105.e1.a1.jsonl | 10,9 KB | 63ca087b91bac5a65906afd4bd8bcfdf5c1e772045cec1ed33c75c045527abde |