Tarefa causcibench_real.q085

Veredito

Pergunta feita ao analista

Does receiving a Chinese COVID-19 vaccine cause an increase in an individual's favorability toward China?

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
   urdinez_undermining— The data comes from an online panel survey conducted across 6 countries: Argentina, Brazil, Chile, Colombia, Mexico, and Peru. The survey was conducted in two waves, before Chinese COVID-19 vaccines were available and after vaccination campaigns were being carried out extensively. In the first round, the survey gathered socio-demographic information and participants' views on the six countries. Meanwhile, the second wave contained vaccine-corrected questions, such as whether respondents had received the Chinese Covid-19 vaccine. Both surveys asked the participants' attitudes towards China and the USA. Data variables: wave: survey year (2020, 2021) duration: time spent by respondents in the survey country: numeric country code device: survey device used sex: 0=female, 1=male age: respondents' age agerecode: age group code vaccine: vaccination status south_korea_score: favorability score assigned to South Korea china_score: favorability score assigned to China usa_score: favorability score assigned to the U.S. japan_score: favorability score assigned to Japan ideology: political ideology education: highest education level region_ar: Argentina subregion code region_br: Brazil subregion code region_co: Colombia subregion code region_cl: Chile subregion code region_mx: Mexico subregion code region_pe: Peru subregion code delta_china: change in favorability score for China delta_usa: change in favorability score for the U.S. income: respondents' income region_code: numeric region identifier country_name: name of the country respondent_code: unique respondent identifier news_experiment: treatment in survey experiment both_waves: indicator if respondent answered both waves excluded: indicator for excluded observations
2. 2Chamada que gerou a resposta finalinfer.ols(Regressão linear por mínimos quadrados ordinários (MQO))citada na respostacoincide com a referência

   Ver chamada call_ddcf679b nos detalhes

   causcibench_metadata_rule/1: did

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

   dataset:

   "urdinez_undermining"

   fe:

   ["respondent_code","wave"]

   intercept:

   true

   level:

   0,950.95

   se:

   "HC1"

   x:

   ["vaccine"]

   y:

   "china_score"

   Resultado calculado

   • coef.vaccine = 3,97535 3.9753489844147873
     Coefficient on vaccine in the regression of china_score
   Diagnósticos: n_obs = 4.4454445 (observations used) · k = 2.8612861 (parameters estimated, fixed-effect dummies included) · r2 = 0,7837540.7837543883864699 · df_resid = 1.5841584 · se_type = "HC1" (heteroskedasticity-robust standard errors (HC1)) · fixed_effects = "respondent_code (2859 levels), wave (2 levels)" (absorbed as dummies; their coefficients are not tabulated)
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: 3,975
   Figuras citadas: call_ddcf679b:coef.vaccine
   • ✓
     #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

   Does receiving a Chinese COVID-19 vaccine cause an increase in an individual's favorability toward China?

   urdinez_undermining (agent)

   The data comes from an online panel survey conducted across 6 countries: Argentina, Brazil, Chile, Colombia, Mexico, and Peru. The survey was conducted in two waves, before Chinese COVID-19 vaccines were available and after vaccination campaigns were being carried out extensively. In the first round, the survey gathered socio-demographic information and participants' views on the six countries. Meanwhile, the second wave contained vaccine-corrected questions, such as whether respondents had received the Chinese Covid-19 vaccine. Both surveys asked the participants' attitudes towards China and the USA. Data variables: wave: survey year (2020, 2021) duration: time spent by respondents in the survey country: numeric country code device: survey device used sex: 0=female, 1=male age: respondents' age agerecode: age group code vaccine: vaccination status south_korea_score: favorability score assigned to South Korea china_score: favorability score assigned to China usa_score: favorability score assigned to the U.S. japan_score: favorability score assigned to Japan ideology: political ideology education: highest education level region_ar: Argentina subregion code region_br: Brazil subregion code region_co: Colombia subregion code region_cl: Chile subregion code region_mx: Mexico subregion code region_pe: Peru subregion code delta_china: change in favorability score for China delta_usa: change in favorability score for the U.S. income: respondents' income region_code: numeric region identifier country_name: name of the country respondent_code: unique respondent identifier news_experiment: treatment in survey experiment both_waves: indicator if respondent answered both waves excluded: indicator for excluded observations

   file 0d804a4b724492f48aeb435e50c458467d1633762f28d7877e8d78bf85daf286 · frame —
2. 1Turno do modelo
   call_6eca2377referenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

   Texto visível

   causcibench_metadata_rule/1: did

   Chamadas pedidas

   • infer.ols

     {
       "controls": [],
       "dataset": "urdinez_undermining",
       "fe": [
         "respondent_code",
         "wave"
       ],
       "x": [
         "vaccine"
       ],
       "y": "china_score"
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
3. 2Ferramenta
   infer.ols— Regressão linear por mínimos quadrados ordinários (MQO)v0.1.0call_ddcf679bdecidido pelo modeloreexecutável

   Motivo: causcibench_metadata_rule/1: did

   Argumentos

   {
     "cluster": null,
     "controls": [],
     "dataset": "urdinez_undermining",
     "fe": [
       "respondent_code",
       "wave"
     ],
     "intercept": true,
     "level": 0.95,
     "se": "HC1",
     "subset": null,
     "weights": null,
     "x": [
       "vaccine"
     ],
     "y": "china_score"
   }

   Figuras call_ddcf679b

   Chave	Valor	Valor bruto	Erro-padrão	IC
   coef.vaccine Coefficient on vaccine in the regression of china_score	3,97535	3.9753489844147873	1,95631.9562961797160763	0,138147 – 7,81255

   Diagnósticos

   • n_obs = 4.4454445 (observations used)
   • k = 2.8612861 (parameters estimated, fixed-effect dummies included)
   • r2 = 0,7837540.7837543883864699
   • df_resid = 1.5841584
   • se_type = "HC1" (heteroskedasticity-robust standard errors (HC1))
   • n_clusters = — (errors not clustered)
   • fixed_effects = "respondent_code (2859 levels), wave (2 levels)" (absorbed as dummies; their coefficients are not tabulated)

   Derivação matemática e pressupostos

   coef.vaccine

   1. Coeficiente de vaccine na regressão por mínimos quadrados ordinários de china_score, efeitos fixos de respondent_code, wave.→ 3,97535 (3.9753489844147873)
   2. Erro-padrão robusto à heterocedasticidade (HC1): a raiz da variância estimada do coeficiente.ep = √V_jj→ 1,9563 (1.9562961797160763)
   3. Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 1584 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": 79.24300151456248,
         "ci_low": -23.408499410775654,
         "coef": 27.91725105189341,
         "p": 0.28618656789293045,
         "se": 26.167078780058173,
         "t": 1.0668845111273573,
         "term": "Intercept"
       },
       {
         "ci_high": 7.812551080802441,
         "ci_low": 0.13814688802713304,
         "coef": 3.9753489844147873,
         "p": 0.04231220031595482,
         "se": 1.9562961797160763,
         "t": 2.032079306616927,
         "term": "vaccine"
       }
     ]
   }

   Hashes SHA-256 e metadados de reprodução

   Digests dos dados

   urdinez_undermining be8c21837f47851acc322a376c4d699f08336d9143f56b79277184ea265367ee

   sha256 do resultado

   032918eeb0c1e4a3a12e8cb295fbff61f4ccf91fd962c803396d598a729e381a

   Resultado mostrado ao modelo
4. 3Turno do modelo
   call_61d59b37referenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

   Chamadas pedidas

   • submit_answer (meta-ferramenta)

     {
       "answer": "Estimativa (infer.ols): coef.vaccine = 3,975.",
       "caveats": [],
       "figure_refs": [
         "call_ddcf679b:coef.vaccine"
       ]
     }

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

   Números encontrados

   3,975

   Sem lastro

   —

   Não citados

   —

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

   Estimativa (infer.ols): coef.vaccine = 3,975.

   Figuras citadas: call_ddcf679b:coef.vaccine · Parada: answered

   Figura da resposta: call_ddcf679b:coef.vaccine = 3,975353.9753489844147873 (se 1,95631.9562961797160763)
7. 6Pontuação
   #0resposta com lastropassouobrigatóriaevery number backed
8. 7Pontuação
   #1figura da resposta próximapassouobrigatória3.975348984 vs 3.96

Épocas × verificações

Tentativas

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

• época 1, tentativa 1passoupontuada(exibindo)

Downloads desta tarefa