Tarefa causcibench_real.q125

Veredito

Pergunta feita ao analista

What is the causal effect of having a sow insured on the subsequent number of sows owned by a farmer?

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
   cai_effect_microinsurance— The dataset is the result of a randomized controlled trial conducted to study the effect of insurance on farming behavior. Each village consists of at least one AHW (Animal Husbandry Worker), who is involved in tasks such as animal immunization, providing technical assistance to farmers, etc. As part of the experiment, AHWs are assigned to different incentive schemes associated with the number of sow insurance purchases. In the control group villages, AHWs were offered a reward of 50 yuan to participate. In the low incentive group (LIG), the AHW is offered 20 Yuan to begin with, and an additional payment of 2 Yuan for each insured sow. Finally, in the high incentive group (HIG), the AHWs are offered a starting payment of 20 Yuan and an additional 4 Yuan for each insured sow. At the end of the experiment, data on total sows in each village were obtained. Variables: sow0: No. of Sows in Dec. 2006 sow1: No. of Sows in Sept. 2007 sow2: No. of Sows in Dec. 2007 pig: No. of Pigs in Dec. 2006 goat: No. of Goats in Dec 2006 pop_regist: Village Population pop_migout: No. of Villagers as Migrant Workers age_vil: Ave. Villager Age edu_vil: Ave. Villager Education (Years) male_vil: Fraction Male in Village land: Land per Household (Mu) house_val: House Value (RMB yuan) lhouse_val: Log House Value surname_vil: No. of Surnames in the Village no_xnh: No. of Villagers in New Medical Coop. Scheme subsidy: No. of Households Receiving Gov. Subsidy insuredpig: No. of Insured Sows snow: No. of Sow Deaths in Snow Storm sow3: No. of Sows in March 2008 sow4: No. of Sows in June 2008 group: Coding for different groups (1 = Control Group; 2 = Low incentive Group; 3 = High Incentive Group) groupd1: Control Group Dummy groupd2: Low Incentive Group Dummy groupd3: High Incentive Dummy villageid: Village Id villagename: Village Name (in Chinese) snow_insured: No. of Sow Deaths in Snow Storm Interact with No. of Insured Sows towndummy1-26: 26 Town dummies sow2008: No. of sows at the end of 2008 sow2009: No. of sows at the end of 2009 sow2010: No. of sows at the end of 2010 sow2011: No. of sows at the end of 2011 pig2008: No. of pigs at the end of 2008 pig2009: No. of pigs at the end of 2009 pig2010: No. of pigs at the end of 2010 pig2011: No. of pigs at the end of 2011 sheep2008: No. of sheep at the end of 2008 cow2008: No. of cows at the end of 2008 sow200810: Average sows 2008-2010 pig200810: Average pigs 2008-2010 lnhouse: Log House Value insured_house: Insured Sows _ Log House Value insured_edu: Insured Sows _ Avg. Villager Education snow_house: Sow Deaths _ Log House Value snow_edu: Sow Deaths _ Avg. Villager Education insured_house_snow: insured_house _ snow insured_edu_snow: insured_edu _ snow house_groupd2: Log House Value _ Low Incentive Dummy house_groupd3: Log House Value _ High Incentive Dummy house_snow_gd2: snow_house _ Low Incentive Dummy house_snow_gd3: snow_house _ High Incentive Dummy edu_groupd2: Avg. Villager Education _ Low Incentive Dummy edu_groupd3: Avg. Villager Education _ High Incentive Dummy edu_snow_gd2: insured_edu _ snow _ Low Incentive Dummy edu_snow_gd3: insured_edu _ snow _ High Incentive Dummy
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_c7aee20c nos detalhes

   causcibench_metadata_rule/1: iv

   Por que este é o caminho de referência: causcibench_metadata_rule/1: iv
   Argumentos principais

   dataset:

   "cai_effect_microinsurance"

   endog:

   ["insuredpig"]

   instruments:

   ["groupd2","groupd3"]

   level:

   0,950.95

   se:

   "HC1"

   y:

   "sow3"

   Resultado calculado

   • coef.insuredpig = 0,904271 0.9042705727198661
     2SLS effect of insuredpig on sow3
   Diagnósticos: first_stage_F.insuredpig = 14,78914.788955277415837 (Wald F of the excluded instruments in the first stage for insuredpig (heteroskedasticity-robust standard errors (HC1))) · partial_r2.insuredpig = 0,04184280.04184280964985643 (share of the variation the instruments explain beyond the controls) · weak_instrument = false (every first-stage F is at least 10) · overid_p = 0,2225650.22256487265624544 (Sargan test of the overidentifying restrictions (assumes homoskedastic errors)) · sargan = 1,487761.4877565346306791 (n·R² of the structural residuals on the instruments) · sargan_df = 11 · n_obs = 321321 · 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: 0,9043
   Figuras citadas: call_c7aee20c:coef.insuredpig
   • ✓
     #0resposta com lastro

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

     CauSciBench effect accuracy: |answer figure − gold| ≤ 5% of |gold|

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 causal effect of having a sow insured on the subsequent number of sows owned by a farmer?

   cai_effect_microinsurance (agent)

   The dataset is the result of a randomized controlled trial conducted to study the effect of insurance on farming behavior. Each village consists of at least one AHW (Animal Husbandry Worker), who is involved in tasks such as animal immunization, providing technical assistance to farmers, etc. As part of the experiment, AHWs are assigned to different incentive schemes associated with the number of sow insurance purchases. In the control group villages, AHWs were offered a reward of 50 yuan to participate. In the low incentive group (LIG), the AHW is offered 20 Yuan to begin with, and an additional payment of 2 Yuan for each insured sow. Finally, in the high incentive group (HIG), the AHWs are offered a starting payment of 20 Yuan and an additional 4 Yuan for each insured sow. At the end of the experiment, data on total sows in each village were obtained. Variables: sow0: No. of Sows in Dec. 2006 sow1: No. of Sows in Sept. 2007 sow2: No. of Sows in Dec. 2007 pig: No. of Pigs in Dec. 2006 goat: No. of Goats in Dec 2006 pop_regist: Village Population pop_migout: No. of Villagers as Migrant Workers age_vil: Ave. Villager Age edu_vil: Ave. Villager Education (Years) male_vil: Fraction Male in Village land: Land per Household (Mu) house_val: House Value (RMB yuan) lhouse_val: Log House Value surname_vil: No. of Surnames in the Village no_xnh: No. of Villagers in New Medical Coop. Scheme subsidy: No. of Households Receiving Gov. Subsidy insuredpig: No. of Insured Sows snow: No. of Sow Deaths in Snow Storm sow3: No. of Sows in March 2008 sow4: No. of Sows in June 2008 group: Coding for different groups (1 = Control Group; 2 = Low incentive Group; 3 = High Incentive Group) groupd1: Control Group Dummy groupd2: Low Incentive Group Dummy groupd3: High Incentive Dummy villageid: Village Id villagename: Village Name (in Chinese) snow_insured: No. of Sow Deaths in Snow Storm Interact with No. of Insured Sows towndummy1-26: 26 Town dummies sow2008: No. of sows at the end of 2008 sow2009: No. of sows at the end of 2009 sow2010: No. of sows at the end of 2010 sow2011: No. of sows at the end of 2011 pig2008: No. of pigs at the end of 2008 pig2009: No. of pigs at the end of 2009 pig2010: No. of pigs at the end of 2010 pig2011: No. of pigs at the end of 2011 sheep2008: No. of sheep at the end of 2008 cow2008: No. of cows at the end of 2008 sow200810: Average sows 2008-2010 pig200810: Average pigs 2008-2010 lnhouse: Log House Value insured_house: Insured Sows _ Log House Value insured_edu: Insured Sows _ Avg. Villager Education snow_house: Sow Deaths _ Log House Value snow_edu: Sow Deaths _ Avg. Villager Education insured_house_snow: insured_house _ snow insured_edu_snow: insured_edu _ snow house_groupd2: Log House Value _ Low Incentive Dummy house_groupd3: Log House Value _ High Incentive Dummy house_snow_gd2: snow_house _ Low Incentive Dummy house_snow_gd3: snow_house _ High Incentive Dummy edu_groupd2: Avg. Villager Education _ Low Incentive Dummy edu_groupd3: Avg. Villager Education _ High Incentive Dummy edu_snow_gd2: insured_edu _ snow _ Low Incentive Dummy edu_snow_gd3: insured_edu _ snow _ High Incentive Dummy

   file e3249019db8a6d5cd72c88756b60b22681a35b469f71e420f6e49bfa747360da · frame —
2. 1Turno do modelo
   call_72c50921referenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

   Texto visível

   causcibench_metadata_rule/1: iv

   Chamadas pedidas

   • infer.iv_2sls

     {
       "dataset": "cai_effect_microinsurance",
       "endog": [
         "insuredpig"
       ],
       "exog": [],
       "instruments": [
         "groupd2",
         "groupd3"
       ],
       "y": "sow3"
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
3. 2Ferramenta
   infer.iv_2sls— Variáveis instrumentais por mínimos quadrados em dois estágios (MQ2E)v0.1.0call_c7aee20cdecidido pelo modeloreexecutável

   Motivo: causcibench_metadata_rule/1: iv

   Argumentos

   {
     "cluster": null,
     "dataset": "cai_effect_microinsurance",
     "endog": [
       "insuredpig"
     ],
     "exog": [],
     "instruments": [
       "groupd2",
       "groupd3"
     ],
     "level": 0.95,
     "se": "HC1",
     "subset": null,
     "y": "sow3"
   }

   Figuras call_c7aee20c

   Chave	Valor	Valor bruto	Erro-padrão	IC
   coef.insuredpig 2SLS effect of insuredpig on sow3	0,904271	0.9042705727198661	0,2412220.2412222374993142	0,429683 – 1,37886

   Diagnósticos

   • first_stage_F.insuredpig = 14,78914.788955277415837 (Wald F of the excluded instruments in the first stage for insuredpig (heteroskedasticity-robust standard errors (HC1)))
   • partial_r2.insuredpig = 0,04184280.04184280964985643 (share of the variation the instruments explain beyond the controls)
   • weak_instrument = false (every first-stage F is at least 10)
   • overid_p = 0,2225650.22256487265624544 (Sargan test of the overidentifying restrictions (assumes homoskedastic errors))
   • sargan = 1,487761.4877565346306791 (n·R² of the structural residuals on the instruments)
   • sargan_df = 11
   • n_obs = 321321
   • se_type = "HC1" (heteroskedasticity-robust standard errors (HC1))
   • n_clusters = — (errors not clustered)

   Derivação matemática e pressupostos

   coef.insuredpig

   1. Coeficiente de insuredpig por mínimos quadrados em dois estágios: sow3 sobre as projeções dos regressores endógenos nos instrumentos ['groupd2', 'groupd3']; resíduos com os valores observados de insuredpig.→ 0,904271 (0.9042705727198661)
   2. Erro-padrão robusto à heterocedasticidade (HC1): a raiz da variância estimada do coeficiente.ep = √V_jj→ 0,241222 (0.2412222374993142)
   3. Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 319 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": 28.996355826296394,
         "ci_low": 7.2363395372191395,
         "coef": 18.116347681757766,
         "p": 0.0011688553608086173,
         "se": 5.530065651674729,
         "t": 3.2759733469477705,
         "term": "Intercept"
       },
       {
         "ci_high": 1.3788580503281227,
         "ci_low": 0.4296830951116095,
         "coef": 0.9042705727198661,
         "p": 0.00021099046920036314,
         "se": 0.2412222374993142,
         "t": 3.7487031962483846,
         "term": "insuredpig"
       }
     ],
     "first_stage": [
       {
         "ci_high": 12.91370831962822,
         "ci_low": 3.248453842533941,
         "coef": 8.08108108108108,
         "endog": "insuredpig",
         "instrument": "groupd2",
         "p": 0.0011144744029620113,
         "se": 2.456287302874883,
         "t": 3.289957600490316
       },
       {
         "ci_high": 16.42106804226038,
         "ci_low": 7.057763390328278,
         "coef": 11.73941571629433,
         "endog": "insuredpig",
         "instrument": "groupd3",
         "p": 0.0000013049416328735358,
         "se": 2.3795510386193643,
         "t": 4.933458255682399
       }
     ],
     "reduced_form": [
       {
         "ci_high": 12.78560862403834,
         "ci_low": -5.028851867281583,
         "coef": 3.8783783783783785,
         "instrument": "groupd2",
         "p": 0.39227447641644997,
         "se": 4.527292397328624,
         "t": 0.8566662008989867
       },
       {
         "ci_high": 18.447556790645507,
         "ci_low": 2.693827368079692,
         "coef": 10.5706920793626,
         "instrument": "groupd3",
         "p": 0.008691791686644738,
         "se": 4.003586832119105,
         "t": 2.6403054367544505
       }
     ]
   }

   • 159 rows were dropped because one of 'sow3', 'insuredpig', 'groupd2', 'groupd3' was missing

   Hashes SHA-256 e metadados de reprodução

   Digests dos dados

   cai_effect_microinsurance cdd6961e1779d8abe1e6f2ed9404f07595871ef43fe70583c3308e396368b57a

   sha256 do resultado

   a6b58ea4c56962ccf0e870ff00181e73a839ed2dd152e2f293a4b36a190c9e18

   Resultado mostrado ao modelo
4. 3Turno do modelo
   call_b08130f9referenceFim: 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.insuredpig = 0,9043.",
       "caveats": [],
       "figure_refs": [
         "call_c7aee20c:coef.insuredpig"
       ]
     }

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

   Números encontrados

   0,9043

   Sem lastro

   —

   Não citados

   —

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

   Estimativa (infer.iv_2sls): coef.insuredpig = 0,9043.

   Figuras citadas: call_c7aee20c:coef.insuredpig · Parada: answered

   Figura da resposta: call_c7aee20c:coef.insuredpig = 0,9042710.9042705727198661 (se 0,2412220.2412222374993142)
7. 6Pontuação
   #0resposta com lastropassouobrigatóriaevery number backed
8. 7Pontuação
   #1figura da resposta próximapassouobrigatória0.9042705727 vs 0.904

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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