Tarefa causcibench_real.q106

Veredito

Pergunta feita ao analista

Does the number of users who purchase premium change after the price of premium is altered?

Valores esperados e de onde vêm

Verificações

Caminho de referência e caminho do agente

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Trajetória

Cada evento na ordem em que aconteceu: turnos do modelo, chamadas de ferramentas, guardas, transformações e pontuação.

1. 0Início

   Does the number of users who purchase premium change after the price of premium is altered?

   enache_demand_for (agent)

   The dataset consists of user-level information, such as total number of purchases and total videos watched, associated with five ""freemium"" mobile app games in six European markets for the years 2017-2022. The games are available on both iOS and Android platforms. However, only Apple changed its pricing tiers in between, as a result of which the prices of the games changed on the iOS platform. Data Variables: game: Anonymized codenames of the games (W1-W5) country: Two-letter country code (France, Germany, Great Britain, Italy, Netherlands, and Sweden) platform: Gaming platform (Android or ios) dt: Date norm_users: Number of users normalized norm_purch_users: Normalized number of users who purchase premium norm_videos: Number of rewarded videos watched purchsmooth: Number of purchases smoothed year: Year recorded month: Month recorded (1-12) apple: Dummy variable for ios platform (1 = platform is ios, 0 = otherwise) google: Dummy variable for android platform (1 = platform is android, 0 = otherwise) post: Indicator of the time period after price change (1 = post price change, 0 = pre price change) window: Not applicable userssmooth: Number of users smoothed postxapple: Interaction between post and apple forpan: Concatenation of game codename, country, and platform cgn: Concatenation of country and game codename

   file d1385394b66f1abe6a205119f2b6c743d2af84dc7ccfe92a5207249d8d3454e4 · frame —
2. 1Turno do modelo
   call_41f4bd1ereferenceFim: 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": [
         "year",
         "month"
       ],
       "dataset": "enache_demand_for",
       "fe": [
         "game",
         "country",
         "apple",
         "post"
       ],
       "x": [
         "postxapple"
       ],
       "y": "norm_purch_users"
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
3. 2Ferramenta
   infer.olsv0.1.0call_ec882079decidido pelo modeloreexecutável

   Motivo: causcibench_metadata_rule/1: did

   Argumentos

   {
     "cluster": null,
     "controls": [
       "year",
       "month"
     ],
     "dataset": "enache_demand_for",
     "fe": [
       "game",
       "country",
       "apple",
       "post"
     ],
     "intercept": true,
     "level": 0.95,
     "se": "HC1",
     "subset": null,
     "weights": null,
     "x": [
       "postxapple"
     ],
     "y": "norm_purch_users"
   }

   Figuras call_ec882079

   Chave	Valor	Valor bruto	Erro-padrão	IC
   coef.postxapple Coefficient on postxapple in the regression of norm_purch_users	0,133584	0.13358400959555405	0,02233970.022339696786359893	0,0897948 – 0,177373

   Diagnósticos

   • n_obs = 12.65512655
   • k = 1414
   • r2 = 0,04348090.04348092310526652
   • df_resid = 12.64112641
   • se_type = "HC1"
   • n_clusters = —
   • fixed_effects = "game (5 levels), country (6 levels), apple (2 levels), post (2 levels)"

   Tabelas (1)

   {
     "coefficients": [
       {
         "ci_high": 1.3886494462938832,
         "ci_low": 1.2525471267935013,
         "coef": 1.3205982865436923,
         "p": 7.318080017428006e-300,
         "se": 0.03471729300050978,
         "t": 38.03863067677255,
         "term": "Intercept"
       },
       {
         "ci_high": 0.1773732034925864,
         "ci_low": 0.08979481569852171,
         "coef": 0.13358400959555405,
         "p": 2.296255101734753e-9,
         "se": 0.022339696786359893,
         "t": 5.979669772291511,
         "term": "postxapple"
       },
       {
         "ci_high": -0.029397038681686395,
         "ci_low": -0.047244411356875425,
         "coef": -0.03832072501928091,
         "p": 4.260552698892277e-17,
         "se": 0.004552548911204195,
         "t": -8.417421924884865,
         "term": "month"
       }
     ]
   }

   • 1745 rows were dropped because one of 'norm_purch_users', 'postxapple', 'year', 'month', 'game', 'country', 'apple', 'post' was missing
   • dropped as collinear with the other regressors: ['year']

   Digests dos dados

   enache_demand_for 2b83258fea6a0022e7fea3285df6717f25a080e24b31c0fc50b042f3c75a9357

   sha256 do resultado

   ec18bbc3455834d6641c9b7e73ec5d2d48889c3aa89cb8541ffc21f908080c63

   Resultado mostrado ao modelo
4. 3Turno do modelo
   call_dd6a4a7freferenceFim: 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.postxapple = 0,1336.",
       "caveats": [],
       "figure_refs": [
         "call_ec882079:coef.postxapple"
       ]
     }

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

   Números encontrados

   0,1336

   Sem lastro

   —

   Não citados

   —

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

   Estimativa (infer.ols): coef.postxapple = 0,1336.

   Figuras citadas: call_ec882079:coef.postxapple · Parada: answered

   Figura da resposta: call_ec882079:coef.postxapple = 0,1335840.13358400959555405 (se 0,02233970.022339696786359893)
7. 6Pontuação
   #0resposta com lastropassouobrigatóriaevery number backed
8. 7Pontuação
   #1figura da resposta próximapassouobrigatória0.1335840096 vs 0.133

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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