Tarefa causcibench_real.q032

Veredito

Pergunta feita ao analista

What is the effect of sanctions imposition on mass mobilization?

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 is the effect of sanctions imposition on mass mobilization?

   tan_causal_effect (agent)

   This dataset is a country-year panel containing political, economic, conflict, and sanctions data, alongside measures of mass mobilization. The data was collected to study whether the imposition of economic sanctions affects domestic political stability, proxied by mass mobilization. Variables: country: Country name year: Calendar year treat: Indicator = 1 if sanctions imposed in year t mm: Mass mobilization index polity: Regime type score EcGI: Economic globalization index lgdp: Log GDP interwar: Interstate war indicator intrawar: Intrastate war indicator lcinc: Log Composite Index of National Capability efindex: Economic freedom index dem: Democracy dummy lowEcGI: Low economic globalization dummy first_sanction: Year of first sanction ever_sanctioned: 1 if country ever sanctioned in dataset period

   file ec5125a0cd4b71a89f79610bc4761f043c3af0ba9ee7f963898acfb357c61c9f · frame —
2. 1Turno do modelo
   call_e61568aareferenceFim: 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": [
         "polity",
         "EcGI",
         "lgdp",
         "interwar",
         "intrawar",
         "lcinc",
         "efindex"
       ],
       "dataset": "tan_causal_effect",
       "fe": [
         "country",
         "year"
       ],
       "x": [
         "treat"
       ],
       "y": "mm"
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
3. 2Ferramenta
   infer.olsv0.1.0call_77c6af8edecidido pelo modeloreexecutável

   Motivo: causcibench_metadata_rule/1: did

   Argumentos

   {
     "cluster": null,
     "controls": [
       "polity",
       "EcGI",
       "lgdp",
       "interwar",
       "intrawar",
       "lcinc",
       "efindex"
     ],
     "dataset": "tan_causal_effect",
     "fe": [
       "country",
       "year"
     ],
     "intercept": true,
     "level": 0.95,
     "se": "HC1",
     "subset": null,
     "weights": null,
     "x": [
       "treat"
     ],
     "y": "mm"
   }

   Figuras call_77c6af8e

   Chave	Valor	Valor bruto	Erro-padrão	IC
   coef.treat Coefficient on treat in the regression of mm	0,200047	0.20004709910396212	0,03819170.03819166272991616	0,125172 – 0,274922

   Diagnósticos

   • n_obs = 4.6104610
   • k = 170170
   • r2 = 0,6961450.6961454471587991
   • df_resid = 4.4404440
   • se_type = "HC1"
   • n_clusters = —
   • fixed_effects = "country (119 levels), year (44 levels)"

   Tabelas (1)

   {
     "coefficients": [
       {
         "ci_high": 1.8342699709287063,
         "ci_low": -0.7238024696760291,
         "coef": 0.5552337506263386,
         "p": 0.39478264570829025,
         "se": 0.6524035955620451,
         "t": 0.8510586918945553,
         "term": "Intercept"
       },
       {
         "ci_high": 0.2749217936602541,
         "ci_low": 0.12517240454767015,
         "coef": 0.20004709910396212,
         "p": 1.6985968099443888e-7,
         "se": 0.03819166272991616,
         "t": 5.23797826029873,
         "term": "treat"
       },
       {
         "ci_high": 0.046316216751039065,
         "ci_low": 0.03294342949604764,
         "coef": 0.03962982312354335,
         "p": 9.08062786248666e-31,
         "se": 0.0034105580238299312,
         "t": 11.619747515405269,
         "term": "polity"
       },
       {
         "ci_high": -0.007892122178756938,
         "ci_low": -0.017982227954941945,
         "coef": -0.012937175066849441,
         "p": 5.168552466047285e-7,
         "se": 0.0025733521785756236,
         "t": -5.027362820587697,
         "term": "EcGI"
       },
       {
         "ci_high": 0.30013737821581515,
         "ci_low": 0.06999019680184532,
         "coef": 0.18506378750883024,
         "p": 0.0016272638035541493,
         "se": 0.05869608939903539,
         "t": 3.1529151158726356,
         "term": "lgdp"
       },
       {
         "ci_high": 0.03990995094346368,
         "ci_low": -0.07657822083448046,
         "coef": -0.01833413494550839,
         "p": 0.5371821000034863,
         "se": 0.02970881547451953,
         "t": -0.6171277667139942,
         "term": "interwar"
       },
       {
         "ci_high": 0.6942305352328709,
         "ci_low": 0.4397684518240378,
         "coef": 0.5669994935284544,
         "p": 3.3429241572840375e-18,
         "se": 0.0648972935695621,
         "t": 8.73687425687019,
         "term": "intrawar"
       },
       {
         "ci_high": 0.1930673490500744,
         "ci_low": -0.024290351174322253,
         "coef": 0.08438849893787606,
         "p": 0.12800121083907165,
         "se": 0.05543429611241596,
         "t": 1.5223156936410536,
         "term": "lcinc"
       },
       {
         "ci_high": 0.5321844580223714,
         "ci_low": -1.0803193419615265,
         "coef": -0.27406744196957755,
         "p": 0.5051722382945897,
         "se": 0.41124843075916145,
         "t": -0.666427933751993,
         "term": "efindex"
       }
     ]
   }

   • 4620 rows were dropped because one of 'mm', 'treat', 'polity', 'EcGI', 'lgdp', 'interwar', 'intrawar', 'lcinc', 'efindex', 'country', 'year' was missing

   Digests dos dados

   tan_causal_effect e0a0728337446eae3980f74d98e844afbc26d3f26fdd8dc6a1963ca1e7f67b62

   sha256 do resultado

   31eaff85920cc6a7395f9f68cc1dfe143e8428de166ebd7c09cd441ef45974dd

   Resultado mostrado ao modelo
4. 3Turno do modelo
   call_5830352freferenceFim: 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.treat = 0,2000.",
       "caveats": [],
       "figure_refs": [
         "call_77c6af8e:coef.treat"
       ]
     }

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

   Números encontrados

   0,2000

   Sem lastro

   —

   Não citados

   —

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

   Estimativa (infer.ols): coef.treat = 0,2000.

   Figuras citadas: call_77c6af8e:coef.treat · Parada: answered

   Figura da resposta: call_77c6af8e:coef.treat = 0,2000470.20004709910396212 (se 0,03819170.03819166272991616)
7. 6Pontuação
   #0resposta com lastropassouobrigatóriaevery number backed
8. 7Pontuação
   #1figura da resposta próximapassouobrigatória0.2000470991 vs 0.2

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)

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