Tarefa crb_papers.q033
Veredito
Família do método (das chamadas de ferramentas): ols (regression)
Pergunta feita ao analista
Did states that adopted direct primaries in 1912 experience an increase in the log of the state-average daily candidate newspaper advertisements (week before the election) from 1910 to 1912 relative to states that had not yet adopted?
Estimativa ingênua (infer.ols): coef.prim_sw = 1,021.
Valores esperados e de onde vêm
crb_papers.q033.gold
- Fonte
- CausalReasoningBenchmark, causal_queries.json, index 33 (revision edd0fe1c1d1c)
- Tipo
- benchmark_gold
- Localização
- causal_queries.json['33'].effect and .standard_error
- Trecho
index 33: effect 0.61347, standard_error 0.223833
- Valor como publicado
0.61347038637508- Erro-padrão como publicado
0.22383298763880474- Valor
- 0,61347
0.61347038637508(se 0,2238330.22383298763880474) - Conferido por
- arquivo do benchmark · Read from the catalog at the pinned revision by scripts/freeze_crb_papers.py; the catalog states the value its estimation script prints.
- Endereço
https://huggingface.co/datasets/syrgkanislab/CausalReasoningBenchmark/resolve/edd0fe1c1d1c5c0a4f0e3e67a50481ac679fb888/causal_queries.json- Licença
- MIT
- #2 figura da resposta próxima: 0,61347
0.61347038637508· relativa 0.000001 · absoluta 2.2383298763880474e-7 - #3 figura da resposta próxima: 0,223833
0.22383298763880474· relativa 0.000001 · absoluta 2.2383298763880474e-7
crb_papers.q033.script
- Fonte
- CausalReasoningBenchmark reference estimation script for index 33
- Tipo
- software
- Localização
- solutions/research_papers/DiD/Hirano_etal_2022/question_2/estimation.py
- Valor
- —
- Conferido por
- arquivo do benchmark · sha256 of the script's 1742 bytes at the pinned revision; the reference path of the item is a transcription of this script into typed transforms and tool calls (never executed here).
- Implementação
- independente da ferramenta
- sha256 da cópia lida
21726561292a6bc9d1ceffe81ded25dedb9065022434ba3e8605839c9231e6d5(identifica a cópia lida, não um arquivo canônico)- Endereço
https://huggingface.co/datasets/syrgkanislab/CausalReasoningBenchmark/resolve/edd0fe1c1d1c5c0a4f0e3e67a50481ac679fb888/solutions/research_papers/DiD/Hirano_etal_2022/question_2/estimation.py- Licença
- MIT
Verificações
| # | Verificação | Obrigatória | Resultado | Esperado | Observado | Tolerância | Fonte | Chamada |
|---|---|---|---|---|---|---|---|---|
| 0 | resposta com lastro every number backed | obrigatória | passou | — | [] | — | — | — |
| 1 | figura da resposta próxima 1.020609492 in [0.1747577306, 1.052183042]? | obrigatória | passou | [0.1747577306030227,1.0521830421471372] | 1,020611.0206094924859823 | interval · [0.1747577306030227, 1.0521830421471372] | — | call_dff064c4 |
| 2 | figura da resposta próxima 1.020609492 vs 0.61347038637508 | registrada | falhou | 0,613470.61347038637508 | 1,020611.0206094924859823 | relativa 0.000001 · absoluta 2.2383298763880474e-7 | crb_papers.q033.gold | call_dff064c4 |
| 3 | figura da resposta próxima 0.04099967176 vs 0.22383298763880474 | registrada | falhou | 0,2238330.22383298763880474 | 0,04099970.040999671762948706 | relativa 0.000001 · absoluta 2.2383298763880474e-7 | crb_papers.q033.gold | call_dff064c4 |
Caminho de referência e caminho do agente
A chamada de referência ao lado da chamada que deu a resposta, argumento por argumento.
caminho de referência do item · benchmark_script
| Argumento | Referência | Agente | |
|---|---|---|---|
| cluster | "state" | — | diferente |
| controls | ["Treated","Post"] | [] | diferente |
| dataset | "ref_panel" | "ds_0017" | diferente |
| fe | — | [] | não fixado pela referência |
| intercept | — | true | não fixado pela referência |
| level | — | 0,950.95 | não fixado pela referência |
| se | — | "HC1" | não fixado pela referência |
| subset | — | — | não fixado pela referência |
| weights | — | — | não fixado pela referência |
| x | ["Treated_Post"] | ["prim_sw"] | diferente |
| y | "ln_x_cand_all" | "ln_x_cand_all" | igual |
Trajetória
Cada evento na ordem em que aconteceu: turnos do modelo, chamadas de ferramentas, guardas, transformações e pontuação.
- 0Início
Did states that adopted direct primaries in 1912 experience an increase in the log of the state-average daily candidate newspaper advertisements (week before the election) from 1910 to 1912 relative to states that had not yet adopted?
ds_0017(agent)| Variable Name | Description | | --- | --- | | (index) | Row index from the CSV export; carries no substantive information. | | state | Two-letter U.S. state postal abbreviation identifying the state associated with the observation. | | year | Calendar year in which the state held the referenced general election. Most elections occurred in November of even-numbered years, though some states held major general elections in other months or in odd-numbered years; the value here reflects the actual calendar year of that election. | | ln_x_cand_all | Log-transformed average number of newspaper advertisements per day placed by or on behalf of individual candidates in the seven days preceding the general election; constructed as ln(ads per newspaper-day + 1). Source counts come from hand-coding full newspaper issues, then averaging ad counts to the newspaper-year and aggregating to a state-year mean. Included offices typically comprise statewide executive posts, state legislatures, U.S. House, judicial, and many local offices; advertisements focused solely on parties are tracked separately, and candidate ads for U.S. Senate and the presidency are excluded from this summary measure. | | prim_sw | Indicator for whether, in that year, the state used state-administered direct primary elections to nominate candidates for statewide offices (1 = direct primaries in use; 0 = nomination by caucus/convention or other non-primary procedures). Reflects the institutional status in each state-year during the late 19th and early 20th centuries. | Dataset Context: This state-year panel assembles historical evidence on campaign communications during an era when newspapers were the dominant mass medium. Researchers reviewed issues from 90 local newspapers across 33 mostly non-Southern states between 1880 and 1930, focusing on the week immediately preceding each state’s general election. Political advertisements were hand-coded at the ad level, distinguishing candidate-focused content from party-only notices. To make newspapers comparable despite different publication schedules and occasional missing issues, counts were normalized to advertisements per newspaper-day, averaged to the newspaper-year, and then aggregated to a state-year mean. The offices captured in the primary advertising measure are those commonly elected across states throughout the period: statewide executives (e.g., governor, attorney general), state legislatures, the U.S. House, judicial posts, and many local offices. Candidate advertising for U.S. Senate and for the presidency was excluded from that main summary to keep the measure focused on offices most consistently affected by state-level electoral rules of the time. The log transformation adds one before taking logs so that years with zero observed advertising are retained on a continuous scale. The panel spans a period of substantial institutional change often associated with Progressive Era reforms. States transitioned at different times to practices such as direct primary elections for nominating statewide candidates, and the dataset records whether a state employed direct primaries in each year. Because election timing varied across states—some held major contests in odd-numbered years or in months other than November—the year variable records the actual calendar year of the pertinent general election. Overall, the dataset provides a harmonized view of newspaper-based campaign activity across places and time, facilitating comparisons of advertising intensity during a formative phase in U.S. electoral development.
filea1cd03905fff0a36614036650735de9568deb88305b615380f0334a23c7d923b· frame —ref_adopt(reference)filea1cd03905fff0a36614036650735de9568deb88305b615380f0334a23c7d923b· frame —select{"columns":["state","year","ln_x_cand_all","prim_sw"]}dropna{"columns":["state","year","ln_x_cand_all","prim_sw"]}filter{"query":"prim_sw == 1"}collapse{"aggs":[{"column":"year","fn":"min","name":"adopt_year"}],"by":["state"]}
ref_panel(reference)filea1cd03905fff0a36614036650735de9568deb88305b615380f0334a23c7d923b· frame —select{"columns":["state","year","ln_x_cand_all","prim_sw"]}dropna{"columns":["state","year","ln_x_cand_all","prim_sw"]}filter{"query":"year in [1910, 1912]"}merge{"how":"left","on":["state"],"with":"ref_adopt"}merge{"how":"left","on":["state"],"with":"ref_years"}merge{"how":"inner","on":["state"],"with":"ref_pre"}merge{"how":"inner","on":["state"],"with":"ref_post"}filter{"query":"nyears == 2 and p_pre == 0 and ((adopt_year == 1912 and p_post == 1) or ((adopt_year != adopt_year or adopt_year > 1912) and p_post == 0))"}derive{"args":{"cmp":"eq","column":"p_post","value":1},"name":"Treated","op":"indicator"}derive{"args":{"cmp":"eq","column":"year","value":1912},"name":"Post","op":"indicator"}derive{"args":{"columns":["Treated","Post"]},"name":"Treated_Post","op":"interaction"}
ref_post(reference)filea1cd03905fff0a36614036650735de9568deb88305b615380f0334a23c7d923b· frame —select{"columns":["state","year","ln_x_cand_all","prim_sw"]}dropna{"columns":["state","year","ln_x_cand_all","prim_sw"]}filter{"query":"year == 1912"}select{"columns":["state","prim_sw"]}rename{"map":{"prim_sw":"p_post"}}
ref_pre(reference)filea1cd03905fff0a36614036650735de9568deb88305b615380f0334a23c7d923b· frame —select{"columns":["state","year","ln_x_cand_all","prim_sw"]}dropna{"columns":["state","year","ln_x_cand_all","prim_sw"]}filter{"query":"year == 1910"}select{"columns":["state","prim_sw"]}rename{"map":{"prim_sw":"p_pre"}}
ref_years(reference)filea1cd03905fff0a36614036650735de9568deb88305b615380f0334a23c7d923b· frame —select{"columns":["state","year","ln_x_cand_all","prim_sw"]}dropna{"columns":["state","year","ln_x_cand_all","prim_sw"]}filter{"query":"year in [1910, 1912]"}collapse{"aggs":[{"column":"ln_x_cand_all","fn":"count","name":"n"}],"by":["state","year"]}collapse{"aggs":[{"column":"n","fn":"count","name":"nyears"}],"by":["state"]}
- 1Turno do modelo
call_c88bd431naive-olsFim:tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cacheTexto visívelLinha de base ingênua: regressão do resultado no tratamento, sem controles.
Chamadas pedidasinfer.ols{ "dataset": "ds_0017", "x": [ "prim_sw" ], "y": "ln_x_cand_all" }
- 2Ferramenta
infer.olsv0.1.0call_dff064c4decidido pelo modeloreexecutávelMotivo: Linha de base ingênua: regressão do resultado no tratamento, sem controles.
Argumentos
{ "cluster": null, "controls": [], "dataset": "ds_0017", "fe": [], "intercept": true, "level": 0.95, "se": "HC1", "subset": null, "weights": null, "x": [ "prim_sw" ], "y": "ln_x_cand_all" }Figuras call_dff064c4 Chave Valor Valor bruto Erro-padrão IC coef.prim_sw Coefficient on prim_sw in the regression of ln_x_cand_all1,02061 1.02060949248598230,0409997 0.0409996717629487060,940137 – 1,10108 Diagnósticosn_obs= 854854k= 22r2= 0,4724140.47241425747224075df_resid= 852852se_type="HC1"n_clusters= —
Tabelas (1)
{ "coefficients": [ { "ci_high": 0.3089938421577919, "ci_low": 0.23606762503033543, "coef": 0.27253073359406366, "p": 1.3055500613185812e-43, "se": 0.01857754022834457, "t": 14.669904101634055, "term": "Intercept" }, { "ci_high": 1.10108168978413, "ci_low": 0.9401372951878345, "coef": 1.0206094924859823, "p": 3.192319434678205e-103, "se": 0.040999671762948706, "t": 24.89311373971302, "term": "prim_sw" } ] }- 45 rows were dropped because one of 'ln_x_cand_all', 'prim_sw' was missing
- Digests dos dados
ds_0017f94064123b5fc42bd483d30636a96ea348e90e5edb4aebf0e6d1b71ed7d043e6- sha256 do resultado
7253ca9486b0d1843cd53ac22fb3831cea4ffc07c4d55fd7a36c6c359c580535
- 3Turno do modelo
call_c6aa7486naive-olsFim:tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cacheChamadas pedidassubmit_answer(meta-ferramenta){ "answer": "Estimativa ingênua (infer.ols): coef.prim_sw = 1,021.", "caveats": [], "figure_refs": [ "call_dff064c4:coef.prim_sw" ] }
- 4Guardaresposta aceita
- Números encontrados
- 1,021
- Sem lastro
- —
- Não citados
- —
1 nova(s) tentativa(s) restante(s) - 5Resposta
Estimativa ingênua (infer.ols): coef.prim_sw = 1,021.
Figuras citadas:call_dff064c4:coef.prim_sw· Parada:answeredFigura da resposta:call_dff064c4:coef.prim_sw= 1,020611.0206094924859823(se 0,04099970.040999671762948706) - 6Pontuação#0resposta com lastropassouobrigatória
every number backed - 7Pontuação#1figura da resposta próximapassouobrigatória
1.020609492 in [0.1747577306, 1.052183042]? - 8Pontuação#2figura da resposta próximafalhouregistrada
1.020609492 vs 0.61347038637508 - 9Pontuação#3figura da resposta próximafalhouregistrada
0.04099967176 vs 0.22383298763880474
Épocas × verificações
| # | 0 | 1 | 2 | 3 |
|---|---|---|---|---|
| Época 1 | passou | passou | falhou | falhou |
Tentativas
Cada tentativa é seu próprio arquivo e é publicada; só a última de cada época é pontuada.
- época 1, tentativa 1passoupontuada(exibindo)
Downloads desta tarefa
50e5ece41a14d1e8005fb5f9bd9eb7a5a613c263ed21bab5bcd551c8abb84d17O hash de um zip é só de transporte: o que ancora a integridade é o RUN_SHA256SUMS que vai dentro dele, cópia exata do SHA256SUMS da execução.
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|---|---|---|
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| sources/crb_papers.q033.script.json | 1,1 KB | dabfdba8eec45c6adbc84417b5c4b7d4c733bd915c661fa65e1a12f968719abb |
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