Tarefa qrdata_socsci.363

Veredito

Pergunta feita ao analista

What is the Local Average Treatment Effect (LATE) of receiving the marketing push on in-app purchases, as estimated using linear regression and instrumental variable, rounded to two decimal places?

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
   app_engagement_push— A study is conducted to measure the effect of a marketing push on user engagement, specifically in-app purchases. Some customers who were assigned to receive the push are not receiving it, because they probably have an older phone that doesn’t support the kind of push the marketing team designed. The dataset app_engagement_push.csv contains records for 10,000 random customers. Each record includes whether an in-app purchase was made (in_app_purchase), if a marketing push was assigned to the user (push_assigned), and if the marketing push was successfully delivered (push_delivered).
2. 2Chamada que gerou a resposta finalinfer.iv_2sls(Variáveis instrumentais por mínimos quadrados em dois estágios (MQ2E))citada na respostacoincide com a referência

   Ver chamada call_fdab1841 nos detalhes

   2SLS of in-app purchases on delivery instrumented by assignment: the LATE.

   Por que este é o caminho de referência: 2SLS of in-app purchases on delivery instrumented by assignment: the LATE.
   Argumentos principais

   dataset:

   "app_engagement_push"

   endog:

   ["push_delivered"]

   instruments:

   ["push_assigned"]

   level:

   0,950.95

   se:

   "HC1"

   y:

   "in_app_purchase"

   Resultado calculado

   • coef.push_delivered = 3,29384 3.2938401294874557
     2SLS effect of push_delivered on in_app_purchase
   Diagnósticos: first_stage_F.push_delivered = 12.556,812556.815498423806 (Wald F of the excluded instruments in the first stage for push_delivered (heteroskedasticity-robust standard errors (HC1))) · partial_r2.push_delivered = 0,5623440.5623437420594468 (share of the variation the instruments explain beyond the controls) · weak_instrument = false (every first-stage F is at least 10) · n_obs = 10.00010000 · 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: 3,294
   Figuras citadas: call_fdab1841:coef.push_delivered
   • ✓
     #0resposta com lastro

     every number of the answer is stated by a tool call
   • ✓
     #1figura da resposta próxima

     QRData eval.py: strictly inside gold ± 3% (a '%' gold divided by 100), on the answer figure after meta.answer_format

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 Local Average Treatment Effect (LATE) of receiving the marketing push on in-app purchases, as estimated using linear regression and instrumental variable, rounded to two decimal places?

   app_engagement_push (agent)

   A study is conducted to measure the effect of a marketing push on user engagement, specifically in-app purchases. Some customers who were assigned to receive the push are not receiving it, because they probably have an older phone that doesn’t support the kind of push the marketing team designed. The dataset app_engagement_push.csv contains records for 10,000 random customers. Each record includes whether an in-app purchase was made (in_app_purchase), if a marketing push was assigned to the user (push_assigned), and if the marketing push was successfully delivered (push_delivered).

   file 3c3a97cca6fdd96f6856f7856b5f8bc786a31d1a9be78c9f610ef3070783e970 · frame —
2. 1Turno do modelo
   call_5f7d8e1breferenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

   Texto visível

   2SLS of in-app purchases on delivery instrumented by assignment: the LATE.

   Chamadas pedidas

   • infer.iv_2sls

     {
       "dataset": "app_engagement_push",
       "endog": [
         "push_delivered"
       ],
       "instruments": [
         "push_assigned"
       ],
       "y": "in_app_purchase"
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
3. 2Ferramenta
   infer.iv_2sls— Variáveis instrumentais por mínimos quadrados em dois estágios (MQ2E)v0.1.0call_fdab1841decidido pelo modeloreexecutável

   Motivo: 2SLS of in-app purchases on delivery instrumented by assignment: the LATE.

   Argumentos

   {
     "cluster": null,
     "dataset": "app_engagement_push",
     "endog": [
       "push_delivered"
     ],
     "exog": [],
     "instruments": [
       "push_assigned"
     ],
     "level": 0.95,
     "se": "HC1",
     "subset": null,
     "y": "in_app_purchase"
   }

   Figuras call_fdab1841

   Chave	Valor	Valor bruto	Erro-padrão	IC
   coef.push_delivered 2SLS effect of push_delivered on in_app_purchase	3,29384	3.2938401294874557	0,7165310.7165310377383689	1,8893 – 4,69839

   Diagnósticos

   • first_stage_F.push_delivered = 12.556,812556.815498423806 (Wald F of the excluded instruments in the first stage for push_delivered (heteroskedasticity-robust standard errors (HC1)))
   • partial_r2.push_delivered = 0,5623440.5623437420594468 (share of the variation the instruments explain beyond the controls)
   • weak_instrument = false (every first-stage F is at least 10)
   • n_obs = 10.00010000
   • se_type = "HC1" (heteroskedasticity-robust standard errors (HC1))
   • n_clusters = — (errors not clustered)

   Derivação matemática e pressupostos

   coef.push_delivered

   1. Com um instrumento e um regressor endógeno, o MQ2E é a razão entre o coeficiente de push_assigned na forma reduzida (in_app_purchase sobre push_assigned) e no primeiro estágio (push_delivered sobre push_assigned).β = ρ / π→ 3,29384 (3.2938401294874557)
   2. Erro-padrão robusto à heterocedasticidade (HC1): a raiz da variância estimada do coeficiente.ep = √V_jj→ 0,716531 (0.7165310377383689)
   3. Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 9998 graus de liberdade vezes o erro-padrão.IC = β ± c · ep

   Pressupostos do estimador

   • the instruments affect the outcome only through the endogenous regressors (exclusion restriction)
   • the instruments are as good as randomly assigned given the controls
   • errors are independent across observations; their variance may differ

   Tabelas (3)

   {
     "coefficients": [
       {
         "ci_high": 70.00206428732744,
         "ci_low": 68.58128552481412,
         "coef": 69.29167490607078,
         "p": 0,
         "se": 0.3624063435926813,
         "t": 191.19884662932293,
         "term": "Intercept"
       },
       {
         "ci_high": 4.698385192093337,
         "ci_low": 1.8892950668815747,
         "coef": 3.2938401294874557,
         "p": 0.000004340276866877566,
         "se": 0.7165310377383689,
         "t": 4.59692596134287,
         "term": "push_delivered"
       }
     ],
     "first_stage": [
       {
         "ci_high": 0.7301329532128219,
         "ci_low": 0.7050278802891,
         "coef": 0.717580416750961,
         "endog": "push_delivered",
         "instrument": "push_assigned",
         "p": 0,
         "se": 0.006403697693101192,
         "t": 112.05719744141297
       }
     ],
     "reduced_form": [
       {
         "ci_high": 3.384745946951474,
         "ci_low": 1.3424443987058479,
         "coef": 2.363595172828661,
         "instrument": "push_assigned",
         "p": 0.000005768223866079881,
         "se": 0.5209417934317078,
         "t": 4.537157898694326
       }
     ]
   }

   Hashes SHA-256 e metadados de reprodução

   Digests dos dados

   app_engagement_push 71f85053598599daf2121fc809836892f772252acf99d24886da3bea76d22aeb

   sha256 do resultado

   0c983d22c46462b16233374f8ce4b76fe915a1f24bd6e36ce66081f2394c9d97

   Resultado mostrado ao modelo
4. 3Turno do modelo
   call_101e995ereferenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

   Chamadas pedidas

   • submit_answer (meta-ferramenta)

     {
       "answer": "Estimativa (infer.iv_2sls): coef.push_delivered = 3,294. Caminho de referência inferido à mão.",
       "caveats": [],
       "figure_refs": [
         "call_fdab1841:coef.push_delivered"
       ]
     }

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

   Números encontrados

   3,294

   Sem lastro

   —

   Não citados

   —

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

   Estimativa (infer.iv_2sls): coef.push_delivered = 3,294. Caminho de referência inferido à mão.

   Figuras citadas: call_fdab1841:coef.push_delivered · Parada: answered

   Figura da resposta: call_fdab1841:coef.push_delivered = 3,293843.2938401294874557 (se 0,7165310.7165310377383689)
7. 6Pontuação
   #0resposta com lastropassouobrigatóriaevery number backed
8. 7Pontuação
   #1figura da resposta próximapassouobrigatória3.29 in (3.1913, 3.3887)?

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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