Tarefa qrdata_socsci.356

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Pergunta feita ao analista

What is the average treatment effect (ATE) on payments from sending the email reminder to late-paying customers? Please choose the variables to adjust for, conduct linear regression, and round your answer to the nearest hundredth.

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1. 0Início

   What is the average treatment effect (ATE) on payments from sending the email reminder to late-paying customers? Please choose the variables to adjust for, conduct linear regression, and round your answer to the nearest hundredth.

   collections_email (agent)

   In a study to determine the causal effect of sending an email reminder on the repayment of debts, a fintech company conducted a random test involving 5000 customers who were late on their payments. Each customer was randomly assigned to either receive an email about negotiating their debt or to be part of a control group that did not receive the email. Data was collected on the amounts paid by the late customers after this intervention. The dataset collections_email.csv contains variables including the amount paid (`payments`), whether the email was sent (`email`), whether the email was opened (`opened`), whether the customer contacted the collections department to negotiate their debt after having received the email (`agreement`), the customer's credit limit before being late (`credit_limit`), and the customer's risk score prior to the delivery of the email (`risk_score`).

   file 3c3a97cca6fdd96f6856f7856b5f8bc786a31d1a9be78c9f610ef3070783e970 · frame —
2. 1Turno do modelo
   call_2df2acfcreferenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

   Texto visível

   OLS of payments on the randomised email with the pre-treatment covariates that predict the outcome; opened and agreement are post-treatment and left out.

   Chamadas pedidas

   • infer.ols

     {
       "controls": [
         "credit_limit",
         "risk_score"
       ],
       "dataset": "collections_email",
       "x": [
         "email"
       ],
       "y": "payments"
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
3. 2Ferramenta
   infer.olsv0.1.0call_4731e412decidido pelo modeloreexecutável

   Motivo: OLS of payments on the randomised email with the pre-treatment covariates that predict the outcome; opened and agreement are post-treatment and left out.

   Argumentos

   {
     "cluster": null,
     "controls": [
       "credit_limit",
       "risk_score"
     ],
     "dataset": "collections_email",
     "fe": [],
     "intercept": true,
     "level": 0.95,
     "se": "HC1",
     "subset": null,
     "weights": null,
     "x": [
       "email"
     ],
     "y": "payments"
   }

   Figuras call_4731e412

   Chave	Valor	Valor bruto	Erro-padrão	IC
   coef.email Coefficient on email in the regression of payments	4,43036	4.430355676950873	2,126362.1263615935509974	0,261754 – 8,59896

   Diagnósticos

   • n_obs = 5.0005000
   • k = 44
   • r2 = 0,4774460.47744586495314767
   • df_resid = 4.9964996
   • se_type = "HC1"
   • n_clusters = —

   Tabelas (1)

   {
     "coefficients": [
       {
         "ci_high": 510.85476126193964,
         "ci_low": 470.87576322409336,
         "coef": 490.8652622430165,
         "p": 0,
         "se": 10.196440549684151,
         "t": 48.14084482238478,
         "term": "Intercept"
       },
       {
         "ci_high": 8.59895772793622,
         "ci_low": 0.2617536259655253,
         "coef": 4.430355676950873,
         "p": 0.03725289982327135,
         "se": 2.1263615935509974,
         "t": 2.0835382328140315,
         "term": "email"
       },
       {
         "ci_high": 0.16771395890570517,
         "ci_low": 0.1344232809018731,
         "coef": 0.15106861990378914,
         "p": 1.0266483629908994e-68,
         "se": 0.008490618469312632,
         "t": 17.792416471166568,
         "term": "credit_limit"
       },
       {
         "ci_high": 71.32185570717414,
         "ci_low": -87.42501083520682,
         "coef": -8.051577564016345,
         "p": 0.8423761617049624,
         "se": 40.48758264566128,
         "t": -0.19886535668187555,
         "term": "risk_score"
       }
     ]
   }

   Digests dos dados

   collections_email a87e53659c327a36822b548e6c2de14c0e3818c8173a93b3a13dacdc5e30958d

   sha256 do resultado

   8fece86199fcd74a115d9e1389f10e26e505c8852838328c68024beeee62ff9e

   Resultado mostrado ao modelo
4. 3Turno do modelo
   call_a3662851referenceFim: 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.email = 4,430. Caminho de referência inferido à mão.",
       "caveats": [],
       "figure_refs": [
         "call_4731e412:coef.email"
       ]
     }

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

   Números encontrados

   4,430

   Sem lastro

   —

   Não citados

   —

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

   Estimativa (infer.ols): coef.email = 4,430. Caminho de referência inferido à mão.

   Figuras citadas: call_4731e412:coef.email · Parada: answered

   Figura da resposta: call_4731e412:coef.email = 4,430364.430355676950873 (se 2,126362.1263615935509974)
7. 6Pontuação
   #0resposta com lastropassouobrigatóriaevery number backed
8. 7Pontuação
   #1figura da resposta próximapassouobrigatória4.43 in (4.2971, 4.5629)?

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• época 1, tentativa 1passoupontuada(exibindo)

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