1990年清洁空气法案修正案健康影响评估数据集

数据集获取（Figshare）

• 源站标题：Evaluation of the Health Impacts of the 1990 Clean Air Act Amendments Using Causal Inference and Machine Learning
• DOI：10.6084/m9.figshare.12851295.v2
• 发布日期：2020-09-16
• 文件数：29 个
• 文件总体积：1.48 MB
• 源站页面：https://tandf.figshare.com/articles/dataset/Evaluation_of_the_health_impacts_of_the_1990_Clean_Air_Act_Amendments_using_causal_inference_and_machine_learning/12851295

命令行下载

curl -L -o ".DS_Store" "https://ndownloader.figshare.com/files/24696525"
curl -L -o ".DS_Store" "https://ndownloader.figshare.com/files/24696528"
curl -L -o ".DS_Store" "https://ndownloader.figshare.com/files/24696531"
curl -L -o ".DS_Store" "https://ndownloader.figshare.com/files/24696534"
curl -L -o ".DS_Store" "https://ndownloader.figshare.com/files/24696537"
curl -L -o ".DS_Store" "https://ndownloader.figshare.com/files/24696540"
curl -L -o ".Rhistory" "https://ndownloader.figshare.com/files/24696543"
curl -L -o ".Rhistory" "https://ndownloader.figshare.com/files/24696546"

文件清单

• .DS_Store（6.15 KB）
• .DS_Store（6.15 KB）
• .DS_Store（6.15 KB）
• .DS_Store（6.15 KB）
• .DS_Store（6.15 KB）
• .DS_Store（6.15 KB）
• .Rhistory（31.34 KB）
• .Rhistory（31.34 KB）
• .Rhistory（31.34 KB）
• README.md（442 B）
• README.md（442 B）
• analysis_wholeUS.R（9.65 KB）
• create_dataset.R（2.02 KB）
• exmatch_trt_v2.R（516 B）
• exmatch_trt_v2.R（516 B）
• figure_2.R（1.52 KB）
• figure_2_verify.pdf（6.51 KB）
• match_rn_app_v2.R（1.01 KB）
• match_rn_app_v2.R（1.01 KB）
• match_rn_boot6.R（1.42 KB）

文件总体积 1.48 MB（源站 API 实测）。

数据加载示例（图像类）

from PIL import Image
import glob, os

files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)

# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path)  # 要求 子目录=类别

目录组织与标注格式以源站说明和下载后实际文件为准。

数据说明

We develop a causal inference approach to estimate the number of adverse health events that were prevented due to changes in exposure to multiple pollutants attributable to a large-scale air quality intervention/regulation, with a focus on the 1990 Clean Air Act Amendments (CAAA). We introduce a causal estimand called the total events avoided (TEA) by the regulation, defined as the difference in the number of health events expected under the no-regulation pollution exposures and the number observed with-regulation. We propose matching and machine learning methods that leverage population-level pollution and health data to estimate the TEA. Our approach improves upon traditional methods for regulation health impact analyses by formalizing causal identifying assumptions, using population-level data, minimizing parametric assumptions, and collectively analyzing multiple pollutants. To reduce model-dependence, our approach estimates cumulative health impacts in the subset of regions with projected no-regulation features lying within the support of the observed with-regulation data, thereby providing a conservative but data-driven assessment to complement traditional parametric approaches. We analyze the health impacts of the CAAA in the U.S. Medicare population in the year 2000, and our estimates suggest that large numbers of cardiovascular and dementia-related hospitalizations were avoided due to CAAA-attributable changes in pollution exposure. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.6084/m9.figshare.12851295.v2（source: https://tandf.figshare.com/articles/dataset/Evaluation_of_the_health_impacts_of_the_1990_Clean_Air_Act_Amendments_using_causal_inference_and_machine_learning/12851295）。