Policy Learning with Asymmetric Counterfactual Utilities

数据集获取（Figshare）

• 源站标题：Policy Learning with Asymmetric Counterfactual Utilities
• DOI：10.6084/m9.figshare.24962436.v3
• 发布日期：2024-12-11
• 文件数：2 个
• 文件总体积：526.37 KB
• 源站页面：https://tandf.figshare.com/articles/dataset/Policy_Learning_with_Asymmetric_Counterfactual_Utilities_/24962436

命令行下载

curl -L -o "uasa_a_2300507_sm9944.pdf" "https://ndownloader.figshare.com/files/51112124"
curl -L -o "uasa_a_2300507_sm6419.pdf" "https://ndownloader.figshare.com/files/43957842"

文件清单

• uasa_a_2300507_sm9944.pdf（409.65 KB）
• uasa_a_2300507_sm6419.pdf（116.72 KB）

文件总体积 526.37 KB（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

Data-driven decision making plays an important role even in high stakes settings like medicine and public policy. Learning optimal policies from observed data requires a careful formulation of the utility function whose expected value is maximized across a population. Although researchers typically use utilities that depend on observed outcomes alone, in many settings the decision maker’s utility function is more properly characterized by the joint set of potential outcomes under all actions. For example, the Hippocratic principle to “do no harm” implies that the cost of causing death to a patient who would otherwise survive without treatment is greater than the cost of forgoing life-saving treatment. We consider optimal policy learning with asymmetric counterfactual utility functions of this form that consider the joint set of potential outcomes. We show that asymmetric counterfactual utilities lead to an unidentifiable expected utility function, and so we first partially identify it. Drawing on statistical decision theory, we then derive minimax decision rules by minimizing the maximum expected utility loss relative to different alternative policies. We show that one can learn minimax loss decision rules from observed data by solving intermediate classification problems, and establish that the finite sample excess expected utility loss of this procedure is bounded by the regret of these intermediate classifiers. We apply this conceptual framework and methodology to the decision about whether or not to use right heart catheterization for patients with possible pulmonary hypertension. Supplementary materials for this article are available online.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.6084/m9.figshare.24962436.v3（source: https://tandf.figshare.com/articles/dataset/Policy_Learning_with_Asymmetric_Counterfactual_Utilities_/24962436）。