Tarefa qrdata_socsci.357

Veredito

Pergunta feita ao analista

What is the average treatment effect (ATE) of the new drug on the amount of days the patient stays in the hospital? Please choose the variables to adjust for, conduct linear regression, and round your answer to the nearest hundredth. If the new drug reduces the amount of days the patient stays in the hospital, the answer should be negative.

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 average treatment effect (ATE) of the new drug on the amount of days the patient stays in the hospital? Please choose the variables to adjust for, conduct linear regression, and round your answer to the nearest hundredth. If the new drug reduces the amount of days the patient stays in the hospital, the answer should be negative.

   hospital_treatment (agent)

   The dataset hospital_treatment.csv includes data from a randomized trial conducted by two hospitals. Both of them are conducting randomised trials on a new drug to treat a certain illness. The outcome of interest is days hospitalised. If the treatment is effective, it will lower the amount of days the patient stays in the hospital. For one of the hospitals, the policy regarding the random treatment is to give it to 90% of its patients while 10% get a placebo. The other hospital has a different policy: it gives the drug to a random 10% of its patients and 90% get a placebo. You are also told that the hospital that gives 90% of the true drug and 10% of placebo usually gets more severe cases of the illness to treat. The CSV file contains columns for `hospital` indicating the hospital a patient belongs to, `treatment` signifying if the patient received the new drug or placebo, `severity` reflecting the severity of the illness, and `days` representing the number of days the patient was hospitalized.

   file 3c3a97cca6fdd96f6856f7856b5f8bc786a31d1a9be78c9f610ef3070783e970 · frame —
2. 1Turno do modelo
   call_e2aedf61referenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

   Texto visível

   OLS of days on treatment controlling for severity, the confounder of the two hospitals' assignment rates.

   Chamadas pedidas

   • infer.ols

     {
       "controls": [
         "severity"
       ],
       "dataset": "hospital_treatment",
       "x": [
         "treatment"
       ],
       "y": "days"
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
3. 2Ferramenta
   infer.olsv0.1.0call_9bfab976decidido pelo modeloreexecutável

   Motivo: OLS of days on treatment controlling for severity, the confounder of the two hospitals' assignment rates.

   Argumentos

   {
     "cluster": null,
     "controls": [
       "severity"
     ],
     "dataset": "hospital_treatment",
     "fe": [],
     "intercept": true,
     "level": 0.95,
     "se": "HC1",
     "subset": null,
     "weights": null,
     "x": [
       "treatment"
     ],
     "y": "days"
   }

   Figuras call_9bfab976

   Chave	Valor	Valor bruto	Erro-padrão	IC
   coef.treatment Coefficient on treatment in the regression of days	-7,59117	-7.591172402702414	2,624962.6249609709725212	-12,8181 – -2,36421

   Diagnósticos

   • n_obs = 8080
   • k = 33
   • r2 = 0,7877940.7877938254262911
   • df_resid = 7777
   • se_type = "HC1"
   • n_clusters = —

   Tabelas (1)

   {
     "coefficients": [
       {
         "ci_high": 15.979641533521002,
         "ci_low": 7.348479344149402,
         "coef": 11.664060438835202,
         "p": 7.699849403464067e-7,
         "se": 2.167267580014088,
         "t": 5.381919863702008,
         "term": "Intercept"
       },
       {
         "ci_high": -2.3642073315304195,
         "ci_low": -12.818137473874408,
         "coef": -7.591172402702414,
         "p": 0.004975029731498045,
         "se": 2.6249609709725212,
         "t": -2.891918198650383,
         "term": "treatment"
       },
       {
         "ci_high": 2.661738952434946,
         "ci_low": 1.8863979891214933,
         "coef": 2.2740684707782197,
         "p": 1.0100416888217126e-18,
         "se": 0.1946865666961218,
         "t": 11.680664513066887,
         "term": "severity"
       }
     ]
   }

   Digests dos dados

   hospital_treatment 152ed1783b86a004c82e8702c1bac193bc72cd1e8fa1474fe613ee88ec5f858c

   sha256 do resultado

   3595d5cdaa2247d0e1265979e2dbacea73c40773764936693f103575a364f14a

   Resultado mostrado ao modelo
4. 3Turno do modelo
   call_323d45bdreferenceFim: 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.treatment = -7,591. Caminho de referência inferido à mão.",
       "caveats": [],
       "figure_refs": [
         "call_9bfab976:coef.treatment"
       ]
     }

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

   Números encontrados

   -7,591

   Sem lastro

   —

   Não citados

   —

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

   Estimativa (infer.ols): coef.treatment = -7,591. Caminho de referência inferido à mão.

   Figuras citadas: call_9bfab976:coef.treatment · Parada: answered

   Figura da resposta: call_9bfab976:coef.treatment = -7,59117-7.591172402702414 (se 2,624962.6249609709725212)
7. 6Pontuação
   #0resposta com lastropassouobrigatóriaevery number backed
8. 7Pontuação
   #1figura da resposta próximapassouobrigatória-7.59 in (-7.8177, -7.3623)?

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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