Tarefa qrdata_socsci.373
qrdata_socsciagentT2publicépoca 1, tentativa 1Execução: 20261015T170010Z-suite-qrdata_socsci-scripted-7912b3Veredito
Família do método (das chamadas de ferramentas): did (two-group two-period difference-in-differences)
Pergunta feita ao analista
What is the Average Treatment Effect on the treated (ATT) of Proposition 99 on cigarette sales, as estimated by the difference-in-differences approach? Please round the final answer to the nearest hundredth. Do not consider variables other than cigsale, california, and after_treatment. The answer is positive if the proposition increases the sale.
Estimativa (infer.did_2x2): did = -27,35. Caminho de referência inferido à mão.
Valores esperados e de onde vêm
qrdata.gold.373
- Fonte
- QRData benchmark/QRData.json at de450af45ff7101b328bb064c6b475f73414a7ed (Xiao Liu, Zirui Wu, Xueqing Wu, Pan Lu, Kai-Wei Chang, Yansong Feng. Are LLMs Capable of Data-based Statistical and Causal Reasoning? Benchmarking Advanced Quantitative Reasoning with Data. Findings of the Association for Computational Linguistics: ACL 2024, pages 9215-9235.)
- Tipo
- benchmark_gold
- Localização
- QRData.json item 373 (0-based), field 'answer'
- Trecho
item 373 answer -27.35
- Valor como publicado
-27.35- Valor
- -27,35
-27.35 - Conferido por
- arquivo do benchmark
- sha256 da cópia lida
e076d960f19434adf728aab70480ce34808c06066fcea48eaa38b145d1a111a1(identifica a cópia lida, não um arquivo canônico)- Endereço
https://raw.githubusercontent.com/xxxiaol/QRData/de450af45ff7101b328bb064c6b475f73414a7ed/benchmark/QRData.json- Licença
- CC BY-NC 4.0
- #1 figura da resposta próxima:
[-28.1705,-26.529500000000002]· interval · [-28.1705, -26.529500000000002] ()
qrdata.eval_py
- Fonte
- QRData benchmark/eval.py at de450af45ff7101b328bb064c6b475f73414a7ed (Xiao Liu, Zirui Wu, Xueqing Wu, Pan Lu, Kai-Wei Chang, Yansong Feng. Are LLMs Capable of Data-based Statistical and Causal Reasoning? Benchmarking Advanced Quantitative Reasoning with Data. Findings of the Association for Computational Linguistics: ACL 2024, pages 9215-9235.)
- Tipo
- software
- Localização
- benchmark/eval.py: extract_first_number, calc_acc
- Valor
- —
- Conferido por
- arquivo do benchmark · Vendored byte for byte as suites/data/qrdata_socsci/vendor/benchmark_eval.py.txt; a parity test runs its functions against MAGI's port and the answer_figure_close check.
- Implementação
- independente da ferramenta
- sha256 da cópia lida
d6e5c1f7535ac9413951996c15544beb6f184244acb2da56e1fcd0942837e33d(identifica a cópia lida, não um arquivo canônico)- Endereço
https://raw.githubusercontent.com/xxxiaol/QRData/de450af45ff7101b328bb064c6b475f73414a7ed/benchmark/eval.py- Licença
- CC BY-NC 4.0
Verificações
| # | Verificação | Obrigatória | Resultado | Esperado | Observado | Tolerância | Fonte | Chamada |
|---|---|---|---|---|---|---|---|---|
| 0 | resposta com lastro every number backed | obrigatória | passou | — | [] | — | — | — |
| 1 | figura da resposta próxima -27.35 in (-28.1705, -26.5295)? | obrigatória | passou | [-28.1705,-26.529500000000002] | -27,35-27.35 | interval · [-28.1705, -26.529500000000002] () | qrdata.gold.373 | call_e3405629 |
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 · hand_inferred
| Argumento | Referência | Agente | |
|---|---|---|---|
| cluster | — | — | não fixado pela referência |
| controls | — | [] | não fixado pela referência |
| dataset | "smoking2" | "smoking2" | igual |
| level | — | 0,950.95 | não fixado pela referência |
| post | "after_treatment" | "after_treatment" | igual |
| treated | "california" | "california" | igual |
| y | "cigsale" | "cigsale" | igual |
Trajetória
Cada evento na ordem em que aconteceu: turnos do modelo, chamadas de ferramentas, guardas, transformações e pontuação.
- 0Início
What is the Average Treatment Effect on the treated (ATT) of Proposition 99 on cigarette sales, as estimated by the difference-in-differences approach? Please round the final answer to the nearest hundredth. Do not consider variables other than cigsale, california, and after_treatment. The answer is positive if the proposition increases the sale.
smoking2(agent)To estimate the effect of cigarette taxation on its consumption, data from cigarette sales were collected and analyzed across 39 states in the United States from the years 1970 to 2000. Proposition 99, a Tobacco Tax and Health Protection Act passed in California in 1988, imposed a 25-cent per pack state excise tax on tobacco cigarettes and implemented additional restrictions, including the ban on cigarette vending machines in public areas accessible by juveniles and a ban on the individual sale of single cigarettes. Revenue generated was allocated for environmental and health care programs along with anti-tobacco advertising. We aim to determine if the imposition of this tax and the subsequent regulations led to a reduction in cigarette sales. The data is in the CSV file smoking2.csv.
file3c3a97cca6fdd96f6856f7856b5f8bc786a31d1a9be78c9f610ef3070783e970· frame — - 1Turno do modelo
call_8d879aedreferenceFim:tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cacheTexto visívelDifference in differences of cigarette sales, California against the other states, after against before 1988.
Chamadas pedidasinfer.did_2x2{ "dataset": "smoking2", "post": "after_treatment", "treated": "california", "y": "cigsale" }
- 2Ferramenta
infer.did_2x2v0.1.0call_e3405629decidido pelo modeloreexecutávelMotivo: Difference in differences of cigarette sales, California against the other states, after against before 1988.
Argumentos
{ "cluster": null, "controls": [], "dataset": "smoking2", "level": 0.95, "post": "after_treatment", "treated": "california", "y": "cigsale" }Figuras call_e3405629 Chave Valor Valor bruto Erro-padrão IC did Difference-in-differences effect on cigsale-27,3491 -27.3491110819308684,55225 4.5522462073920575-36,2803 – -18,4179 Diagnósticosdesign="stacked"n_obs= 1.2091209se_type="HC1"parallel_trends="untestable"n_clusters= —
Tabelas (1)
{ "means": [ { "group": "treated", "mean": 116.21052631578948, "n": 19, "period": "before" }, { "group": "treated", "mean": 60.350000063578285, "n": 12, "period": "after" }, { "group": "comparison", "mean": 130.56952911334685, "n": 722, "period": "before" }, { "group": "comparison", "mean": 102.05811394306652, "n": 456, "period": "after" } ] }- with two periods parallel trends cannot be tested
- Digests dos dados
smoking2d54b84cb5b42c225732a5e4ae3b893195557b7a9df43e8a26093cf7beb0601ff- sha256 do resultado
9f3ac52bbdb33510c6462d3d2a3742821282cc0bd65b6fa8c4e80d30d7379f95
- 3Turno do modelo
call_61965b68referenceFim:tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cacheChamadas pedidassubmit_answer(meta-ferramenta){ "answer": "Estimativa (infer.did_2x2): did = -27,35. Caminho de referência inferido à mão.", "caveats": [], "figure_refs": [ "call_e3405629:did" ] }
- 4Guardaresposta aceita
- Números encontrados
- -27,35
- Sem lastro
- —
- Não citados
- —
1 nova(s) tentativa(s) restante(s) - 5Resposta
Estimativa (infer.did_2x2): did = -27,35. Caminho de referência inferido à mão.
Figuras citadas:call_e3405629:did· Parada:answeredFigura da resposta:call_e3405629:did= -27,3491-27.349111081930868(se 4,552254.5522462073920575) - 6Pontuação#0resposta com lastropassouobrigatória
every number backed - 7Pontuação#1figura da resposta próximapassouobrigatória
-27.35 in (-28.1705, -26.5295)?
Épocas × verificações
| # | 0 | 1 |
|---|---|---|
| Época 1 | passou | passou |
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
84b23cbc7e14b7573f10a0513bf8cf758ded8873434ab7f44e24049093e3c293O 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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| attachments/4d88155e106b1134e4b2d31f26402264e7c6ada068b167938d1d7a6f919ee35b.json | 1,1 KB | 4d88155e106b1134e4b2d31f26402264e7c6ada068b167938d1d7a6f919ee35b |
| attachments/52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b.json | 64,0 KB | 52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b |
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| attachments/df54a4631f1a54b9051e93c9a93e8e54bc722690246a9b341b35f327291c50d9.json | 373 B | df54a4631f1a54b9051e93c9a93e8e54bc722690246a9b341b35f327291c50d9 |
| native/qrdata_socsci/qrdata_socsci.373.e1.a1.json | 452 B | 54432a1dc8cfcecf8f5ebb0643496de6bd1d9023a0052bdda4b1766c502ae652 |
| sources/qrdata.eval_py.json | 1,3 KB | d610b61f390d29ce767e08ce850eab5cb0dc4d13c1edb62d8cf3b0e49a3a291a |
| sources/qrdata.gold.373.json | 1,1 KB | f8a1fc32ad4e5555a76d5fc3cf0e21097e3e329b3d8a82aeedd07e1576f41519 |
| specs/qrdata_socsci.373.json | 4,5 KB | 0cd9be43a2a2477b485276e652f9e006b589d5a244053a6a8ea7cc4105c64e38 |
| traces/qrdata_socsci.373.e1.a1.jsonl | 12,7 KB | 96425240768d77fc87b3a445ebea2a70f3c42b0bef29e462f91facde8796f00d |
| trajectories/qrdata_socsci.373.e1.a1.jsonl | 9,9 KB | fe3a23e6b8ac5b2799d02ad79636d7c43650680618c484d0d7d376e665fd262f |