Tarefa causcibench_real.q135

Veredito

Pergunta feita ao analista

How much does providing information about how masks protect the wearer increase the likelihood that someone would encourage others to wear a mask?

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
   bokemper_experimental_evidence— The randomized experiment examines the causal relationship between beliefs, social norms, and reported intentions to engage in mask promoting behavior. Subjects are randomly assigned to a control group, a ""Mask Protects You"" group, and a ""Mask Protects Others"" group. The control group reads a message unrelated to the experiment, the ""Mask Protects You"" group reads a message about how masks protect the wearer, and the ""Mask Protects Others"" group reads a message about how masks protect others. After the treatment, subjects are asked about their beliefs about masks. They then read three vignette scenarios, taking place at an ATM, in a park or in a meeting in their neighborhood. In the first scenario, they are asked about what they would do when they have forgotten to bring their mask. In the second scenario, they are asked about what they would do if someone else was not wearing their mask properly. In the third scenario, they are asked about their evaluation of someone who took action against someone not wearing their mask properly. Variables in the dataset: age: age of the subject female: whether the subject is female lucid_hhi: household income lucid_incmissing: whether the subject's income is missing ethnicity: ethnicity of the subject education: education level of the subject workstatus: work status of the subject fluvaccine: whether the subject has received the flu vaccine pool_self_scenario_atm: whether the first vignette scenario takes place at an ATM pool_self_scenario_meet: whether the first vignette scenario takes place in a meeting in the neighborhood pool_other_scenario_atm: whether the second vignette scenario takes place at an ATM pool_other_scenario_meet: whether the second vignette scenario takes place in a meeting in the neighborhood partisanship_1: whether the subject is a strong democrat partisanship_2: whether the subject is a democrat partisanship_3: whether the subject is a democrat lean partisanship_4: whether the subject is an independent partisanship_5: whether the subject is a republican lean partisanship_6: whether the subject is a republican partisanship_7: whether the subject is a strong republican T_mask_protectyou: whether the subject was in the ""Mask Protects You"" group T_mask_protectother: whether the subject was in the ""Mask Protects Others"" group mskblfs_prtctothers: whether the subject believes that masks protect others after the intervention mskblfs_prtctyou: whether the subject believes that masks protect the wearer after the intervention pool_self: own mask wearing behavior in the vignette scenario, 1 means they would continue the activity without a mask, 2 means they would continue the activity without a mask while keeping distance from others, 3 means they would abandon the activity, 4 means they would go retrieve their mask and then continue the activity pool_other: behavior when someone else is not wearing their mask properly, 1 means they would continue the activity as normal, 2 means they would continue the activity as normal while keeping distance from others, 3 means they would abandon the activity, 4 means they would ask the other person to wear the mask properly pool_self_others: whether others are wearing masks in the first vignette scenario pool_other_others: whether others are wearing masks in the second vignette scenario
2. 2Chamada que gerou a resposta finalinfer.ols(Regressão linear por mínimos quadrados ordinários (MQO))citada na respostacoincide com a referência

   Ver chamada call_0f88dffe nos detalhes

   causcibench_metadata_rule/1: ols

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

   controls:

   ["pool_other_scenario_atm","pool_other_scenario_meet","age","female","lucid_hhi","lucid_incmissing","ethnicity","education","workstatus","fluvaccine","partisanship_1","partisanship_2","partisanship_3","partisanship_5","partisanship_6","partisanship_7"]

   dataset:

   "bokemper_experimental_evidence"

   intercept:

   true

   level:

   0,950.95

   se:

   "HC1"

   x:

   ["T_mask_protectyou"]

   y:

   "pool_other"

   Resultado calculado

   • coef.T_mask_protectyou = -0,0459998 -0.045999795853778766
     Coefficient on T_mask_protectyou in the regression of pool_other
   Diagnósticos: n_obs = 2.8682868 (observations used) · k = 1818 (parameters estimated, fixed-effect dummies included) · r2 = 0,07819140.07819139874431735 · df_resid = 2.8502850 · 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: -0,04600
   Figuras citadas: call_0f88dffe:coef.T_mask_protectyou
   • ✓
     #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

   How much does providing information about how masks protect the wearer increase the likelihood that someone would encourage others to wear a mask?

   bokemper_experimental_evidence (agent)

   The randomized experiment examines the causal relationship between beliefs, social norms, and reported intentions to engage in mask promoting behavior. Subjects are randomly assigned to a control group, a ""Mask Protects You"" group, and a ""Mask Protects Others"" group. The control group reads a message unrelated to the experiment, the ""Mask Protects You"" group reads a message about how masks protect the wearer, and the ""Mask Protects Others"" group reads a message about how masks protect others. After the treatment, subjects are asked about their beliefs about masks. They then read three vignette scenarios, taking place at an ATM, in a park or in a meeting in their neighborhood. In the first scenario, they are asked about what they would do when they have forgotten to bring their mask. In the second scenario, they are asked about what they would do if someone else was not wearing their mask properly. In the third scenario, they are asked about their evaluation of someone who took action against someone not wearing their mask properly. Variables in the dataset: age: age of the subject female: whether the subject is female lucid_hhi: household income lucid_incmissing: whether the subject's income is missing ethnicity: ethnicity of the subject education: education level of the subject workstatus: work status of the subject fluvaccine: whether the subject has received the flu vaccine pool_self_scenario_atm: whether the first vignette scenario takes place at an ATM pool_self_scenario_meet: whether the first vignette scenario takes place in a meeting in the neighborhood pool_other_scenario_atm: whether the second vignette scenario takes place at an ATM pool_other_scenario_meet: whether the second vignette scenario takes place in a meeting in the neighborhood partisanship_1: whether the subject is a strong democrat partisanship_2: whether the subject is a democrat partisanship_3: whether the subject is a democrat lean partisanship_4: whether the subject is an independent partisanship_5: whether the subject is a republican lean partisanship_6: whether the subject is a republican partisanship_7: whether the subject is a strong republican T_mask_protectyou: whether the subject was in the ""Mask Protects You"" group T_mask_protectother: whether the subject was in the ""Mask Protects Others"" group mskblfs_prtctothers: whether the subject believes that masks protect others after the intervention mskblfs_prtctyou: whether the subject believes that masks protect the wearer after the intervention pool_self: own mask wearing behavior in the vignette scenario, 1 means they would continue the activity without a mask, 2 means they would continue the activity without a mask while keeping distance from others, 3 means they would abandon the activity, 4 means they would go retrieve their mask and then continue the activity pool_other: behavior when someone else is not wearing their mask properly, 1 means they would continue the activity as normal, 2 means they would continue the activity as normal while keeping distance from others, 3 means they would abandon the activity, 4 means they would ask the other person to wear the mask properly pool_self_others: whether others are wearing masks in the first vignette scenario pool_other_others: whether others are wearing masks in the second vignette scenario

   file e94fe873cb4358fa045bc38ef25d976d69a12ed882c571b03327e0a5b7be7723 · frame —
2. 1Turno do modelo
   call_582a140ereferenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

   Texto visível

   causcibench_metadata_rule/1: ols

   Chamadas pedidas

   • infer.ols

     {
       "controls": [
         "pool_other_scenario_atm",
         "pool_other_scenario_meet",
         "age",
         "female",
         "lucid_hhi",
         "lucid_incmissing",
         "ethnicity",
         "education",
         "workstatus",
         "fluvaccine",
         "partisanship_1",
         "partisanship_2",
         "partisanship_3",
         "partisanship_5",
         "partisanship_6",
         "partisanship_7"
       ],
       "dataset": "bokemper_experimental_evidence",
       "x": [
         "T_mask_protectyou"
       ],
       "y": "pool_other"
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
3. 2Ferramenta
   infer.ols— Regressão linear por mínimos quadrados ordinários (MQO)v0.1.0call_0f88dffedecidido pelo modeloreexecutável

   Motivo: causcibench_metadata_rule/1: ols

   Argumentos

   {
     "cluster": null,
     "controls": [
       "pool_other_scenario_atm",
       "pool_other_scenario_meet",
       "age",
       "female",
       "lucid_hhi",
       "lucid_incmissing",
       "ethnicity",
       "education",
       "workstatus",
       "fluvaccine",
       "partisanship_1",
       "partisanship_2",
       "partisanship_3",
       "partisanship_5",
       "partisanship_6",
       "partisanship_7"
     ],
     "dataset": "bokemper_experimental_evidence",
     "fe": [],
     "intercept": true,
     "level": 0.95,
     "se": "HC1",
     "subset": null,
     "weights": null,
     "x": [
       "T_mask_protectyou"
     ],
     "y": "pool_other"
   }

   Figuras call_0f88dffe

   Chave	Valor	Valor bruto	Erro-padrão	IC
   coef.T_mask_protectyou Coefficient on T_mask_protectyou in the regression of pool_other	-0,0459998	-0.045999795853778766	0,04130410.0413041399702649	-0,126989 – 0,0349892

   Diagnósticos

   • n_obs = 2.8682868 (observations used)
   • k = 1818 (parameters estimated, fixed-effect dummies included)
   • r2 = 0,07819140.07819139874431735
   • df_resid = 2.8502850
   • se_type = "HC1" (heteroskedasticity-robust standard errors (HC1))
   • n_clusters = — (errors not clustered)

   Derivação matemática e pressupostos

   coef.T_mask_protectyou

   1. Coeficiente de T_mask_protectyou na regressão por mínimos quadrados ordinários de pool_other.→ -0,0459998 (-0.045999795853778766)
   2. Erro-padrão robusto à heterocedasticidade (HC1): a raiz da variância estimada do coeficiente.ep = √V_jj→ 0,0413041 (0.0413041399702649)
   3. Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 2850 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": 2.4796033103138715,
         "ci_low": 2.113213744475238,
         "coef": 2.2964085273945547,
         "p": 3.7322475507301058e-121,
         "se": 0.09342874871767848,
         "t": 24.579249523439575,
         "term": "Intercept"
       },
       {
         "ci_high": 0.034989225827188955,
         "ci_low": -0.1269888175347465,
         "coef": -0.045999795853778766,
         "p": 0.2655083164959083,
         "se": 0.0413041399702649,
         "t": -1.113684872433957,
         "term": "T_mask_protectyou"
       },
       {
         "ci_high": -0.1751543885516158,
         "ci_low": -0.358540524689269,
         "coef": -0.2668474566204424,
         "p": 1.2729139427386915e-8,
         "se": 0.046763169121079175,
         "t": -5.706359548248774,
         "term": "pool_other_scenario_atm"
       },
       {
         "ci_high": 0.012310529766411102,
         "ci_low": -0.18115337935304565,
         "coef": -0.08442142479331728,
         "p": 0.08714245705423893,
         "se": 0.049332984987411496,
         "t": -1.7112571804617023,
         "term": "pool_other_scenario_meet"
       },
       {
         "ci_high": 0.008385091604363673,
         "ci_low": 0.003671058172667347,
         "coef": 0.00602807488851551,
         "p": 5.635911104318725e-7,
         "se": 0.0012020709266886323,
         "t": 5.014741438860986,
         "term": "age"
       },
       {
         "ci_high": 0.1163565475863703,
         "ci_low": -0.041738868973775975,
         "coef": 0.037308839306297165,
         "p": 0.3548079479860949,
         "se": 0.04031407639408586,
         "t": 0.9254543981508759,
         "term": "female"
       },
       {
         "ci_high": 0.010632291051714307,
         "ci_low": -0.0019043748551003659,
         "coef": 0.0043639580983069704,
         "p": 0.17233270967886002,
         "se": 0.0031968295988023215,
         "t": 1.3650893685237113,
         "term": "lucid_hhi"
       },
       {
         "ci_high": 0.1772398168143317,
         "ci_low": -0.25521308568693263,
         "coef": -0.03898663443630046,
         "p": 0.7237092244866707,
         "se": 0.11027479308135106,
         "t": -0.3535407625525041,
         "term": "lucid_incmissing"
       },
       {
         "ci_high": 0.11915707156867814,
         "ci_low": 0.027168311228880533,
         "coef": 0.07316269139877933,
         "p": 0.0018326939671245313,
         "se": 0.023456985613020634,
         "t": 3.1190150604077522,
         "term": "ethnicity"
       },
       {
         "ci_high": 0.0648825068669966,
         "ci_low": 0.005349199316006775,
         "coef": 0.03511585309150169,
         "p": 0.020784673642066837,
         "se": 0.015180897465741127,
         "t": 2.3131605473752765,
         "term": "education"
       },
       {
         "ci_high": 0.002432661437118802,
         "ci_low": -0.06070182310114183,
         "coef": -0.029134580832011515,
         "p": 0.07044884426345455,
         "se": 0.016099191792877596,
         "t": -1.8096921390116536,
         "term": "workstatus"
       },
       {
         "ci_high": 0.05091988707700937,
         "ci_low": 0.013416834837250872,
         "coef": 0.03216836095713012,
         "p": 0.0007790497697152157,
         "se": 0.009563217871214025,
         "t": 3.363759080921831,
         "term": "fluvaccine"
       },
       {
         "ci_high": 0.39573713673842514,
         "ci_low": 0.14682689339358257,
         "coef": 0.27128201506600386,
         "p": 0.000019824122980540226,
         "se": 0.06347171084278017,
         "t": 4.274061805864523,
         "term": "partisanship_1"
       },
       {
         "ci_high": 0.3795025357656873,
         "ci_low": 0.08828549173533073,
         "coef": 0.23389401375050903,
         "p": 0.0016514852807553908,
         "se": 0.07425987682465927,
         "t": 3.1496687545385273,
         "term": "partisanship_2"
       },
       {
         "ci_high": 0.4175754663745718,
         "ci_low": 0.09682547019008919,
         "coef": 0.2572004682823305,
         "p": 0.0016801698534504778,
         "se": 0.08179073202077665,
         "t": 3.1446162899849814,
         "term": "partisanship_3"
       },
       {
         "ci_high": 0.07617589035369005,
         "ci_low": -0.29329843657517823,
         "coef": -0.1085612731107441,
         "p": 0.24930790474093095,
         "se": 0.09421535782346445,
         "t": -1.152267269569365,
         "term": "partisanship_5"
       },
       {
         "ci_high": 0.06357402736350205,
         "ci_low": -0.25323972857537924,
         "coef": -0.09483285060593859,
         "p": 0.24054806844348914,
         "se": 0.08078699710284323,
         "t": -1.1738627997920845,
         "term": "partisanship_6"
       },
       {
         "ci_high": -0.13836766272800372,
         "ci_low": -0.4103184865221605,
         "coef": -0.2743430746250821,
         "p": 0.00007804391594385234,
         "se": 0.06934702171900888,
         "t": -3.9560902231203,
         "term": "partisanship_7"
       }
     ]
   }

   • 232 rows were dropped because one of 'pool_other', 'T_mask_protectyou', 'pool_other_scenario_atm', 'pool_other_scenario_meet', 'age', 'female', 'lucid_hhi', 'lucid_incmissing', 'ethnicity', 'education', 'workstatus', 'fluvaccine', 'partisanship_1', 'partisanship_2', 'partisanship_3', 'partisanship_5', 'partisanship_6', 'partisanship_7' was missing

   Hashes SHA-256 e metadados de reprodução

   Digests dos dados

   bokemper_experimental_evidence e2a4c2f6a5449108e627246309dda3cbfe09d0576c9c98eb7dc615e7e153447d

   sha256 do resultado

   b834f9fc7018bb0d3736a712a2ed28444ea3a08304cf6aeb2763e1244c5dd49c

   Resultado mostrado ao modelo
4. 3Turno do modelo
   call_266b20e6referenceFim: 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.T_mask_protectyou = -0,04600.",
       "caveats": [],
       "figure_refs": [
         "call_0f88dffe:coef.T_mask_protectyou"
       ]
     }

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

   Números encontrados

   -0,04600

   Sem lastro

   —

   Não citados

   —

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

   Estimativa (infer.ols): coef.T_mask_protectyou = -0,04600.

   Figuras citadas: call_0f88dffe:coef.T_mask_protectyou · Parada: answered

   Figura da resposta: call_0f88dffe:coef.T_mask_protectyou = -0,0459998-0.045999795853778766 (se 0,04130410.0413041399702649)
7. 6Pontuação
   #0resposta com lastropassouobrigatóriaevery number backed
8. 7Pontuação
   #1figura da resposta próximafalhouobrigatória-0.04599979585 vs 0.038

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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